Publications by authors named "Shireen Elhabian"

29 Publications

  • Page 1 of 1

Leveraging unsupervised image registration for discovery of landmark shape descriptor.

Med Image Anal 2021 Jul 9;73:102157. Epub 2021 Jul 9.

Scientific Computing and Imaging Institute, 72 Central Campus Dr, University of Utah, Salt Lake City, Utah-84112, USA; School of Computing, 50 Central Campus Dr, University of Utah, Salt Lake City, Utah-84112, USA.

In current biological and medical research, statistical shape modeling (SSM) provides an essential framework for the characterization of anatomy/morphology. Such analysis is often driven by the identification of a relatively small number of geometrically consistent features found across the samples of a population. These features can subsequently provide information about the population shape variation. Dense correspondence models can provide ease of computation and yield an interpretable low-dimensional shape descriptor when followed by dimensionality reduction. However, automatic methods for obtaining such correspondences usually require image segmentation followed by significant preprocessing, which is taxing in terms of both computation as well as human resources. In many cases, the segmentation and subsequent processing require manual guidance and anatomy specific domain expertise. This paper proposes a self-supervised deep learning approach for discovering landmarks from images that can directly be used as a shape descriptor for subsequent analysis. We use landmark-driven image registration as the primary task to force the neural network to discover landmarks that register the images well. We also propose a regularization term that allows for robust optimization of the neural network and ensures that the landmarks uniformly span the image domain. The proposed method circumvents segmentation and preprocessing and directly produces a usable shape descriptor using just 2D or 3D images. In addition, we also propose two variants on the training loss function that allows for prior shape information to be integrated into the model. We apply this framework on several 2D and 3D datasets to obtain their shape descriptors. We analyze these shape descriptors in their efficacy of capturing shape information by performing different shape-driven applications depending on the data ranging from shape clustering to severity prediction to outcome diagnosis.
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http://dx.doi.org/10.1016/j.media.2021.102157DOI Listing
July 2021

Uncertain-DeepSSM: From Images to Probabilistic Shape Models.

Shape Med Imaging (2020) 2020 Oct 3;12474:57-72. Epub 2020 Oct 3.

Scientific Computing and Imaging Institute, University of Utah, UT, USA.

Statistical shape modeling (SSM) has recently taken advantage of advances in deep learning to alleviate the need for a time-consuming and expert-driven workflow of anatomy segmentation, shape registration, and the optimization of population-level shape representations. DeepSSM is an end-to-end deep learning approach that extracts statistical shape representation directly from unsegmented images with little manual overhead. It performs comparably with state-of-the-art shape modeling methods for estimating morphologies that are viable for subsequent downstream tasks. Nonetheless, DeepSSM produces an overconfident estimate of shape that cannot be blindly assumed to be accurate. Hence, conveying what DeepSSM does not know, via quantifying granular estimates of uncertainty, is critical for its direct clinical application as an on-demand diagnostic tool to determine how trustworthy the model output is. Here, we propose Uncertain-DeepSSM as a unified model that quantifies both, data-dependent aleatoric uncertainty by adapting the network to predict intrinsic input variance, and model-dependent epistemic uncertainty via a Monte Carlo dropout sampling to approximate a variational distribution over the network parameters. Experiments show an accuracy improvement over DeepSSM while maintaining the same benefits of being end-to-end with little pre-processing.
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http://dx.doi.org/10.1007/978-3-030-61056-2_5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8011333PMC
October 2020

VAEs: Fixing Sample Generation for Regularized VAEs.

Comput Vis ACCV 2020 Nov-Dec;12625:643-660. Epub 2021 Feb 25.

Scientific Computing and Imaging Institute, School of Computing, University of Utah, Salt Lake City, UT, USA.

Unsupervised representation learning via generative modeling is a staple to many computer vision applications in the absence of labeled data. Variational Autoencoders (VAEs) are powerful generative models that learn representations useful for data generation. However, due to inherent challenges in the training objective, VAEs fail to learn useful representations amenable for downstream tasks. Regularization-based methods that attempt to improve the representation learning aspect of VAEs come at a price: poor sample generation. In this paper, we explore this representation-generation trade-off for regularized VAEs and introduce a new family of priors, namely decoupled priors, or dpVAEs, that decouple the representation space from the generation space. This decoupling enables the use of VAE regularizers on the representation space without impacting the distribution used for sample generation, and thereby reaping the representation learning benefits of the regularizations without sacrificing the sample generation. dpVAE leverages invertible networks to learn a bijective mapping from an arbitrarily complex representation distribution to a simple, tractable, generative distribution. Decoupled priors can be adapted to the state-of-the-art VAE regularizers without additional hyperparameter tuning. We showcase the use of dpVAEs with different regularizers. Experiments on MNIST, SVHN, and CelebA demonstrate, quantitatively and qualitatively, that dpVAE fixes sample generation for regularized VAEs.
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http://dx.doi.org/10.1007/978-3-030-69538-5_39DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7993751PMC
February 2021

An Image-based Approach for 3D Left Atrium Functional Measurements.

Comput Cardiol (2010) 2020 Sep 10;47. Epub 2021 Feb 10.

Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.

There is growing interest in the assessment of function of the left atrium (LA) in patients with atrial fibrillation (AF). Existing methods of LA functional measurement only quantify a limited subset of the functional parameters from a single or biplane CINE-MRI scan through the LA. Here, we propose an image-based method for comprehensive evaluation of the function of the entire LA in 3D. 4D LA images were reconstructed from a series of CINE image stack covering the whole LA with small or no gap between thin slices. A segmentation from a high-resolution Magnetic Resonance Angiography (MRA) was registered and propagated through pairwise deformable registrations covering the cardiac cycle. Volume, LA ejection fraction and surface strain were computed for each timepoint and registered to Late Gadolinium Enhancement (LGE) scans for each of 52 patient scans. A correlation coefficient of -0.11 was calculated between LGE and strain, indicating that fibrotic tissue correlates with reduced elasticity.
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http://dx.doi.org/10.22489/cinc.2020.459DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7992118PMC
September 2020

Right Ventricular Shape Distortion in Tricuspid Regurgitation.

Comput Cardiol (2010) 2020 Sep 10;47. Epub 2021 Feb 10.

University of Utah, Salt Lake City, UT, USA.

Tricuspid regurgitation (TR) is a failure in right-sided AV valve function which, if left untreated, leads to marked cardiac shape changes and heart failure. However, the specific right ventricular shape changes resulting from TR are unknown. The goal of this study is to characterize the RV shape changes of patients with severe TR. RVs were segmented from CINE MRI images. Using particle-based shape modeling (PSM), a dense set of homologous landmarks were placed with geometric consistency on the endocardial surface of each RV, via an entropy-based optimization of the information content of the shape model. Principal component analysis (PCA) identified the significant modes of shape variation across the population. These modes were used to create a patient-prediction model. 32 patients and 6 healthy controls were studied. The mean RV shape of TR patients demonstrated increased sphericity relative to controls, with the three most dominant modes of variation showing significant widening of the short axis of the heart, narrowing of the base at the RV outflow tract (RVOT), and blunting of the RV apex. By PCA, shape changes based on the first three modes of variation correctly identified patient vs. control hearts 86.5% of the time. The shape variation may further illuminate the mechanics of TR-induced RV failure and recovery, providing potential targets for therapies including novel devices and surgical interventions.
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http://dx.doi.org/10.22489/cinc.2020.346DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7992117PMC
September 2020

Combined Estimation of Shape and Pose for Statistical Analysis of Articulating Joints.

Shape Med Imaging (2020) 2020 Oct 3;12474:111-121. Epub 2020 Oct 3.

Scientific Computing and Imaging Institute, University of Utah.

Quantifying shape variations in articulated joints is of utmost interest to understand the underlying joint biomechanics and associated clinical symptoms. For joint comparisons and analysis, the relative positions of the bones can confound subsequent analysis. Clinicians design specific image acquisition protocols to neutralize the individual pose variations. However, recent studies have shown that even specific acquisition protocols fail to achieve consistent pose. The individual pose variations are largely attributed to the day-to-day functioning of the patient, such as gait during walk, as well as interactions between specific morphologies and joint alignment. This paper presents a novel two-step method to neutralize such patient-specific variations while simultaneously preserving the inherent relationship of the articulated joint. The resulting shape models are then used to discover clinically relevant shape variations in a population of hip joints.
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http://dx.doi.org/10.1007/978-3-030-61056-2_9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7962350PMC
October 2020

Small-molecule mimicry hunting strategy in the imperial cone snail, .

Sci Adv 2021 Mar 12;7(11). Epub 2021 Mar 12.

Department of Medicinal Chemistry, College of Pharmacy, University of Utah, Salt Lake City, UT 84112, USA.

Venomous animals hunt using bioactive peptides, but relatively little is known about venom small molecules and the resulting complex hunting behaviors. Here, we explored the specialized metabolites from the venom of the worm-hunting cone snail, Using the model polychaete worm , we demonstrate that venom contains small molecules that mimic natural polychaete mating pheromones, evoking the mating phenotype in worms. The specialized metabolites from different cone snails are species-specific and structurally diverse, suggesting that the cones may adopt many different prey-hunting strategies enabled by small molecules. Predators sometimes attract prey using the prey's own pheromones, in a strategy known as aggressive mimicry. Instead, uses metabolically stable mimics of those pheromones, indicating that, in biological mimicry, even the molecules themselves may be disguised, providing a twist on fake news in chemical ecology.
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http://dx.doi.org/10.1126/sciadv.abf2704DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7954447PMC
March 2021

A Cooperative Autoencoder for Population-Based Regularization of CNN Image Registration.

Med Image Comput Comput Assist Interv 2019 Oct 10;11765:391-400. Epub 2019 Oct 10.

Scientific Computing and Imaging Institute, University of Utah.

Spatial transformations are enablers in a variety of medical image analysis applications that entail aligning images to a common coordinate systems. Population analysis of such transformations is expected to capture the underlying image and shape variations, and hence these transformations are required to produce correspondences. This is usually enforced through some smoothness-based generic metric or regularization of the deformation field. Alternatively, population-based regularization has been shown to produce anatomically accurate correspondences in cases where anatomically unaware (i.e., data independent) regularization fail. Recently, deep networks have been used to generate spatial transformations in an unsupervised manner, and, once trained, these networks are computationally faster and as accurate as conventional, optimization-based registration methods. However, the deformation fields produced by these networks require smoothness penalties, just as the conventional registration methods, and ignores population-level statistics of the transformations. Here, we propose a novel neural network architecture that simultaneously learns and uses the population-level statistics of the spatial transformations to regularize the neural networks for unsupervised image registration. This regularization is in the form of a bottleneck autoencoder, which learns and adapts to the population of transformations required to align input images by encoding the transformations to a low dimensional manifold. The proposed architecture produces deformation fields that describe the population-level features and associated correspondences in an anatomically relevant manner and are statistically compact relative to the state-of-the-art approaches while maintaining computational efficiency. We demonstrate the efficacy of the proposed architecture on synthetic data sets, as well as 2D and 3D medical data.
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http://dx.doi.org/10.1007/978-3-030-32245-8_44DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7425577PMC
October 2019

Interatrial Septum and Appendage Ostium in Atrial Fibrillation Patients: A Population Study.

Comput Cardiol (2010) 2019 Sep 24;46. Epub 2020 Feb 24.

Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.

Left atrial appendage (LAA) closure is performed in atrial fibrillation (AF) patients to help prevent stroke. LAA closure using an occlusion implant is performed under imaging guidance. However, occlusion can be a complicated process due to the highly variable and heterogeneous LAA shapes across patients. Patient-specific implant selection and insertion processes are keys to the success of the procedure, yet subjective in nature. A population study of the angle of entry at the interatrial septum relative to the appendage can assist in both catheter design and patient-specific implant choice. In our population study, we analyzed the inherent clusters of the angles that were obtained between the septum normal and the LAA ostium plane. The number of inherent angle clusters matched the LAA four morphological classifications reported in the literature. Further, our exploratory analysis revealed that the normal from the ostium plane does not intersect the septum in all the samples under study. The insights gained from this study can help assist in making objective decisions during LAA closure.
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http://dx.doi.org/10.22489/cinc.2019.439DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338039PMC
September 2019

Efficient Segmentation Pipeline Using Diffeomorphic Image Registration: A Validation Study.

Comput Cardiol (2010) 2019 Sep 24;46. Epub 2020 Feb 24.

Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, USA.

Functional measurements of the left atrium (LA) in atrial fibrillation (AF) patients is limited to a single CINE slice midway through the LA. Nonetheless, a full 3D characterization of atrial functional measurements would provide more insights into LA function. But this improved modeling capacity comes at a price of requiring LA segmentation of each 3D time point,a time-consuming and expensive task that requires anatomy-specific expertise.We propose an efficient pipeline which requires ground truth segmentation of a single (or limited) CINE time point to accurately propagate it throughout the sequence. This method significantly saves human effort and enable better characterization of LA anatomy. From a gated cardiac CINE MRI sequence we select a single CINE time point with ground truth segmentation, and assuming cyclic motion, we register other images corresponding to all time points using diffeomorphic registration in ANTs. The diffeomorphic registration fields allow us to map a given anatomical shape (segmentation) to each CINE time point, facilitating the construction of a 4D shape model.
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http://dx.doi.org/10.22489/cinc.2019.364DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338038PMC
September 2019

An Optimal, Generative Model for Estimating Multi-Label Probabilistic Maps.

IEEE Trans Med Imaging 2020 07 23;39(7):2316-2326. Epub 2020 Jan 23.

Multi-label probabilistic maps, a.k.a. probabilistic segmentations, parameterize a population of intimately co-existing anatomical shapes and are useful for various medical imaging applications, such as segmentation, anatomical atlases, shape analysis, and consensus generation. Existing methods to estimate probabilistic segmentations rely on ad hoc intermediate representations (e.g., average of Gaussian-smoothed label maps and smoothed signed distance maps) that do not necessarily conform to the underlying generative process. Generative modeling of such maps could help discover as well as aide in the statistical analysis of sub-groups in a population via clustering and mixture modeling techniques. In this paper, we propose an estimation of multi-label probabilistic maps and showcase their favorable performance for modeling anatomical shapes such as the left atrium of the human heart and brain structures. The proposed formulation relies on a constrained optimization in the natural parameter space of the exponential family form of categorical distributions. A smoothness prior provides generalizability in the model and helps achieve greater performance in modeling tasks for unseen samples. We demonstrate and compare the effectiveness of the proposed method for Bayesian image segmentation, multi-atlas segmentation, and shape-based clustering.
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http://dx.doi.org/10.1109/TMI.2020.2968917DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7395849PMC
July 2020

Thinking outside the glenohumeral box: Hierarchical shape variation of the periarticular anatomy of the scapula using statistical shape modeling.

J Orthop Res 2020 10 24;38(10):2272-2279. Epub 2020 Jan 24.

Department of Orthopaedics, University of Utah, Salt Lake City, Utah.

Variation in the shape of the glenoid and periarticular anatomy of the scapula has been associated with shoulder pathology. The goal of this study was to identify the modes of shape variation of periarticular scapular anatomy in relation to the glenoid in nonpathologic shoulders. Computed tomography scans of 31 cadaveric scapulae, verified to be free of pathology, were three-dimensionally reconstructed. Statistical shape modeling and principal component analysis identified the modes of shape variation across the population. Corresponding linear and angular measurements quantified the morphometric variance identified by the modes. Linear measures were normalized to the radius of the inferior glenoid to account for differences in the scaling of the bones. Five modes captured 89.7% of total shape variation of the glenoid and periarticular anatomy. Apart from size differences (mode 1: 33.0%), acromial anatomy accounted for the largest variation (mode 2: 32.0%). Further modes described variation in glenoid inclination (mode 3: 11.8%), coracoid orientation and size (mode 4: 9.0%), and variation in coracoacromial (CA) morphology (mode 5: 3.1%). The average scapula had a mean acromial tilt of 49 ± 7°, scapular spine angle of 61 ± 6°, the glenoid inclination of 84 ± 4°, coracoid deviation angle of 26 ± 4°, coracoid length of 3.7 ± 0.3 glenoid radii, and a CA base length of 5.6 ± 0.5 radii. In this study, the identified shape modes explain almost all of the variance in scapular anatomy. The acromion exhibited the highest variance of all periarticular anatomic structures of the scapula in relation to the glenoid, which may play a role in many shoulder pathologies.
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http://dx.doi.org/10.1002/jor.24589DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7375008PMC
October 2020

Medial axis segmentation of cranial nerves using shape statistics-aware discrete deformable models.

Int J Comput Assist Radiol Surg 2019 Nov 24;14(11):1955-1967. Epub 2019 Jun 24.

Department of Modeling, Simulation and Visualization Engineering, Old Dominion University, Norfolk, USA.

Purpose: We propose a segmentation methodology for brainstem cranial nerves using statistical shape model (SSM)-based deformable 3D contours from T MR images.

Methods: We create shape models for ten pairs of cranial nerves. High-resolution T MR images are segmented for nerve centerline using a 1-Simplex discrete deformable 3D contour model. These segmented centerlines comprise training datasets for the shape model. Point correspondence for the training dataset is performed using an entropy-based energy minimization framework applied to particles located on the centerline curve. The shape information is incorporated into the 1-Simplex model by introducing a shape-based internal force, making the deformation stable against low resolution and image artifacts.

Results: The proposed method is validated through extensive experiments using both synthetic and patient MRI data. The robustness and stability of the proposed method are experimented using synthetic datasets. SSMs are constructed independently for ten pairs (CNIII-CNXII) of brainstem cranial nerves using ten non-pathological image datasets of the brainstem. The constructed ten SSMs are assessed in terms of compactness, specificity and generality. In order to quantify the error distances between segmented results and ground truths, two metrics are used: mean absolute shape distance (MASD) and Hausdorff distance (HD). MASD error using the proposed shape model is 0.19 ± 0.13 (mean ± std. deviation) mm and HD is 0.21 mm which are sub-voxel accuracy given the input image resolution.

Conclusion: This paper described a probabilistic digital atlas of the ten brainstem-attached cranial nerve pairs by incorporating a statistical shape model with the 1-Simplex deformable contour. The integration of shape information as a priori knowledge results in robust and accurate centerline segmentations from even low-resolution MRI data, which is essential in neurosurgical planning and simulations for accurate and robust 3D patient-specific models of critical tissues including cranial nerves.
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http://dx.doi.org/10.1007/s11548-019-02014-zDOI Listing
November 2019

Coracoacromial morphology: a contributor to recurrent traumatic anterior glenohumeral instability?

J Shoulder Elbow Surg 2019 Jul 28;28(7):1316-1325.e1. Epub 2019 Mar 28.

Department of Orthopaedics, University of Utah, Salt Lake City, UT, USA; Scientific Computing and Imaging Institute, School of Computing, University of Utah, Salt Lake City, UT, USA; Department of Bioengineering, University of Utah, Salt Lake City, UT, USA. Electronic address:

Background: Although scapular morphology contributes to glenohumeral osteoarthritis and rotator cuff disease, its role in traumatic glenohumeral instability remains unknown. We hypothesized that coracoacromial and glenoid morphology would differ between healthy subjects and patients with recurrent traumatic anterior shoulder instability.

Methods: Computed tomography scans of 31 cadaveric control scapulae and 54 scapulae of patients with recurrent traumatic anterior shoulder instability and Hill-Sachs lesions were 3-dimensionally reconstructed. Statistical shape modeling identified the modes of variation between the scapulae of both groups. Corresponding measurements quantified these modes in relation to the glenoid center (linear offset measures), defined by the best-fit circle of the inferior glenoid, or the glenoid center plane (angles), which bisects the glenoid longitudinally. Distances were normalized for glenoid size.

Results: Compared with controls, the unstable coracoids were shorter (P = .004), with a more superior and medial offset of the tip (mean difference [MD], 7 and 3 mm, respectively; P < .001) and an origin closer to the 12-o'clock position (MD, 6°; P < .001). The unstable scapular spines originated closer to the 9-o'clock position (MD, 4°; P = .012), and the unstable acromions were more vertically oriented (MD, 6°; P < .001). The unstable glenoids had an increased height-width index (MD, 0.04; P = .021), had a flatter anterior-posterior radius of curvature (MD, 77 mm; P < .001), and were more anteriorly tilted (MD, 5°; P = .005).

Conclusions: Coracoacromial and glenoid anatomy differs between individuals with and without recurrent traumatic anterior shoulder instability. This pathologic anatomy is not addressed by current soft-tissue stabilization procedures and may contribute to instability recurrence.
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http://dx.doi.org/10.1016/j.jse.2019.01.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6591074PMC
July 2019

DeepSSM: A Deep Learning Framework for Statistical Shape Modeling from Raw Images.

Shape Med Imaging (2018) 2018 Sep 23;11167:244-257. Epub 2018 Nov 23.

Scientific Computing and Imaging Institute, University of Utah.

Statistical shape modeling is an important tool to characterize variation in anatomical morphology. Typical shapes of interest are measured using 3D imaging and a subsequent pipeline of registration, segmentation, and some extraction of shape features or projections onto some lower-dimensional shape space, which facilitates subsequent statistical analysis. Many methods for constructing compact shape representations have been proposed, but are often impractical due to the sequence of image preprocessing operations, which involve significant parameter tuning, manual delineation, and/or quality control by the users. We propose DeepSSM: a deep learning approach to extract a low-dimensional shape representation directly from 3D images, requiring virtually no parameter tuning or user assistance. DeepSSM uses a convolutional neural network (CNN) that simultaneously localizes the biological structure of interest, establishes correspondences, and projects these points onto a low-dimensional shape representation in the form of PCA loadings within a point distribution model. To overcome the challenge of the limited availability of training images with dense correspondences, we present a novel data augmentation procedure that uses existing correspondences on a relatively small set of processed images with shape statistics to create plausible training samples with known shape parameters. In this way, we leverage the limited CT/MRI scans (40-50) into thousands of images needed to train a deep neural net. After the training, the CNN automatically produces accurate low-dimensional shape representations for unseen images. We validate DeepSSM for three different applications pertaining to modeling pediatric cranial CT for characterization of metopic craniosynostosis, femur CT scans identifying morphologic deformities of the hip due to femoroacetabular impingement, and left atrium MRI scans for atrial fibrillation recurrence prediction.
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http://dx.doi.org/10.1007/978-3-030-04747-4_23DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6385885PMC
September 2018

On the Evaluation and Validation of Off-the-shelf Statistical Shape Modeling Tools: A Clinical Application.

Shape Med Imaging (2018) 2018 Sep 23;11167:14-27. Epub 2018 Nov 23.

Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA

Statistical shape modeling (SSM) has proven useful in many areas of biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes. Recently, the increased availability of high-resolution in vivo images of anatomy has led to the development and distribution of open-source computational tools to model anatomical shapes and their variability within populations with unprecedented detail and statistical power. Nonetheless, there is little work on the evaluation and validation of such tools as related to clinical applications that rely on morphometric quantifications for treatment planning. To address this lack of validation, we systematically assess the outcome of widely used off-the-shelf SSM tools, namely ShapeWorks, SPHARM-PDM, and Deformetrica, in the context of designing closure devices for left atrium appendage (LAA) in atrial fibrillation (AF) patients to prevent stroke, where an incomplete LAA closure may be worse than no closure. This study is motivated by the potential role of SSM in the geometric design of closure devices, which could be informed by population-level statistics, and patient-specific device selection, which is driven by anatomical measurements that could be automated by relating patient-level anatomy to population-level morphometrics. Hence, understanding the consequences of different SSM tools for the final analysis is critical for the careful choice of the tool to be deployed in real clinical scenarios. Results demonstrate that estimated measurements from ShapeWorks model are more consistent compared to models from Deformetrica and SPHARM-PDM. Furthermore, ShapeWorks and Deformetrica shape models capture clinically relevant population-level variability compared to SPHARM-PDM models.
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http://dx.doi.org/10.1007/978-3-030-04747-4_2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6385871PMC
September 2018

Does Alignment in Statistical Shape Modeling of Left Atrium Appendage Impact Stroke Prediction?

Comput Cardiol (2010) 2019 24;46. Epub 2020 Feb 24.

Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, USA.

Evidence suggests that the shape of left atrium appendages (LAA) is a primary indicator in predicting stroke for patients diagnosed with atrial fibrillation (AF). Statistical shape modeling tools used to represent (i.e., parameterize) the underlying LAA variability are of crucial importance to learn shape-based predictors of stroke. Most shape modeling techniques use some form of alignment either as a data pre-processing step or during the modeling step. However, the LAA is a joint anatomy along with left atrium (LA), and the relative position and alignment plays a crucial part in determining risk of stroke. In this paper, we explore different alignment strategies for statistical shape modeling and how each strategy affects the stroke prediction capability. This allows for identifying a unified approach of alignment while analyzing the LAA anatomy for stroke. Here, we study three different alignment strategies, (i) global alignment, (ii) global translational alignment and (iii) cluster based alignment. Our results show that alignment strategies that take into account LAA orientation, i.e., (ii), or the inherent natural clustering of the population under study, i.e., (iii), provide significant improvement over global alignment in both qualitative as well as quantitative measures.
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http://dx.doi.org/10.22489/cinc.2019.200DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338006PMC
February 2020

Which Two-dimensional Radiographic Measurements of Cam Femoroacetabular Impingement Best Describe the Three-dimensional Shape of the Proximal Femur?

Clin Orthop Relat Res 2019 01;477(1):242-253

P. R. Atkins, J. A. Weiss, S. K. Aoki, C. L. Peters, A. E. Anderson, Department of Orthopaedics, University of Utah, Salt Lake City, UT, USA P. R. Atkins, Y. Shin, R. T. Whitaker, J. A. Weiss, C. L. Peters, A. E. Anderson, Department of Bioengineering, University of Utah, Salt Lake City, UT, USA P. Agrawal, S. Y. Elhabian, R. T. Whitaker, J. A. Weiss, A. E. Anderson, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA P. Agrawal, S. Y. Elhabian, R. T. Whitaker, J. A. Weiss, School of Computing, University of Utah, Salt Lake City, UT, USA.

Background: Many two-dimensional (2-D) radiographic views are used to help diagnose cam femoroacetabular impingement (FAI), but there is little consensus as to which view or combination of views is most effective at visualizing the magnitude and extent of the cam lesion (ie, severity). Previous studies have used a single image from a sequence of CT or MR images to serve as a reference standard with which to evaluate the ability of 2-D radiographic views and associated measurements to describe the severity of the cam lesion. However, single images from CT or MRI data may fail to capture the apex of the cam lesion. Thus, it may be more appropriate to use measurements of three-dimensional (3-D) surface reconstructions from CT or MRI data to serve as an anatomic reference standard when evaluating radiographic views and associated measurements used in the diagnosis of cam FAI.

Questions/purposes: The purpose of this study was to use digitally reconstructed radiographs and 3-D statistical shape modeling to (1) determine the correlation between 2-D radiographic measurements of cam FAI and 3-D metrics of proximal femoral shape; and 2) identify the combination of radiographic measurements from plain film projections that were most effective at predicting the 3-D shape of the proximal femur.

Methods: This study leveraged previously acquired CT images of the femur from a convenience sample of 37 patients (34 males; mean age, 27 years, range, 16-47 years; mean body mass index [BMI], 24.6 kg/m, range, 19.0-30.2 kg/m) diagnosed with cam FAI imaged between February 2005 and January 2016. Patients were diagnosed with cam FAI based on a culmination of clinical examinations, history of hip pain, and imaging findings. The control group consisted of 59 morphologically normal control participants (36 males; mean age, 29 years, range, 15-55 years; mean BMI, 24.4 kg/m, range, 16.3-38.6 kg/m) imaged between April 2008 and September 2014. Of these controls, 30 were cadaveric femurs and 29 were living participants. All controls were screened for evidence of femoral deformities using radiographs. In addition, living control participants had no history of hip pain or previous surgery to the hip or lower limbs. CT images were acquired for each participant and the surface of the proximal femur was segmented and reconstructed. Surfaces were input to our statistical shape modeling pipeline, which objectively calculated 3-D shape scores that described the overall shape of the entire proximal femur and of the region of the femur where the cam lesion is typically located. Digital reconstructions for eight plain film views (AP, Meyer lateral, 45° Dunn, modified 45° Dunn, frog-leg lateral, Espié frog-leg, 90° Dunn, and cross-table lateral) were generated from CT data. For each view, measurements of the α angle and head-neck offset were obtained by two researchers (intraobserver correlation coefficients of 0.80-0.94 for the α angle and 0.42-0.80 for the head-neck offset measurements). The relationships between radiographic measurements from each view and the 3-D shape scores (for the entire proximal femur and for the region specific to the cam lesion) were assessed with linear correlation. Additionally, partial least squares regression was used to determine which combination of views and measurements was the most effective at predicting 3-D shape scores.

Results: Three-dimensional shape scores were most strongly correlated with α angle on the cross-table view when considering the entire proximal femur (r = -0.568; p < 0.001) and on the Meyer lateral view when considering the region of the cam lesion (r = -0.669; p < 0.001). Partial least squares regression demonstrated that measurements from the Meyer lateral and 90° Dunn radiographs produced the optimized regression model for predicting shape scores for the proximal femur (R = 0.405, root mean squared error of prediction [RMSEP] = 1.549) and the region of the cam lesion (R = 0.525, RMSEP = 1.150). Interestingly, views with larger differences in the α angle and head-neck offset between control and cam FAI groups did not have the strongest correlations with 3-D shape.

Conclusions: Considered together, radiographic measurements from the Meyer lateral and 90° Dunn views provided the most effective predictions of 3-D shape of the proximal femur and the region of the cam lesion as determined using shape modeling metrics.

Clinical Relevance: Our results suggest that clinicians should consider using the Meyer lateral and 90° Dunn views to evaluate patients in whom cam FAI is suspected. However, the α angle and head-neck offset measurements from these and other plain film views could describe no more than half of the overall variation in the shape of the proximal femur and cam lesion. Thus, caution should be exercised when evaluating femoral head anatomy using the α angle and head-neck offset measurements from plain film radiographs. Given these findings, we believe there is merit in pursuing research that aims to develop the framework necessary to integrate statistical shape modeling into clinical evaluation, because this could aid in the diagnosis of cam FAI.
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http://dx.doi.org/10.1097/CORR.0000000000000462DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6345307PMC
January 2019

Skeletal Shape Correspondence Through Entropy.

IEEE Trans Med Imaging 2018 01 21;37(1):1-11. Epub 2017 Sep 21.

We present a novel approach for improving the shape statistics of medical image objects by generating correspondence of skeletal points. Each object's interior is modeled by an s-rep, i.e., by a sampled, folded, two-sided skeletal sheet with spoke vectors proceeding from the skeletal sheet to the boundary. The skeleton is divided into three parts: the up side, the down side, and the fold curve. The spokes on each part are treated separately and, using spoke interpolation, are shifted along that skeleton in each training sample so as to tighten the probability distribution on those spokes' geometric properties while sampling the object interior regularly. As with the surface/boundary-based correspondence method of Cates et al., entropy is used to measure both the probability distribution tightness and the sampling regularity, here of the spokes' geometric properties. Evaluation on synthetic and real world lateral ventricle and hippocampus data sets demonstrate improvement in the performance of statistics using the resulting probability distributions. This improvement is greater than that achieved by an entropy-based correspondence method on the boundary points.
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http://dx.doi.org/10.1109/TMI.2017.2755550DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5943061PMC
January 2018

ShapeCut: Bayesian surface estimation using shape-driven graph.

Med Image Anal 2017 Aug 29;40:11-29. Epub 2017 Apr 29.

Scientific Computing and Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, USA. Electronic address:

A variety of medical image segmentation problems present significant technical challenges, including heterogeneous pixel intensities, noisy/ill-defined boundaries and irregular shapes with high variability. The strategy of estimating optimal segmentations within a statistical framework that combines image data with priors on anatomical structures promises to address some of these technical challenges. However, methods that rely on local optimization techniques and/or local shape penalties (e.g., smoothness) have been proven to be inadequate for many difficult segmentation problems. These challenging segmentation problems can benefit from the inclusion of global shape priors within a maximum-a-posteriori estimation framework, which biases solutions toward an object class of interest. In this paper, we propose a maximum-a-posteriori formulation that relies on a generative image model by incorporating both local and global shape priors. The proposed method relies on graph cuts as well as a new shape parameters estimation that provides a global updates-based optimization strategy. We demonstrate our approach on synthetic datasets as well as on the left atrial wall segmentation from late-gadolinium enhancement MRI, which has been shown to be effective for identifying myocardial fibrosis in the diagnosis of atrial fibrillation. Experimental results prove the effectiveness of the proposed approach in terms of the average surface distance between extracted surfaces and the corresponding ground-truth, as well as the clinical efficacy of the method in the identification of fibrosis and scars in the atrial wall.
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http://dx.doi.org/10.1016/j.media.2017.04.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5546629PMC
August 2017

OPTIMAL PARAMETER MAP ESTIMATION FOR SHAPE REPRESENTATION: A GENERATIVE APPROACH.

Proc IEEE Int Symp Biomed Imaging 2016 Apr 16;2016:660-663. Epub 2016 Jun 16.

Scientific Computing and Imaging Institute, University of Utah, USA.

Probabilistic label maps are a useful tool for important medical image analysis tasks such as segmentation, shape analysis, and atlas building. Existing methods typically rely on blurred signed distance maps or smoothed label maps to model uncertainties and shape variabilities, which do not conform to any generative model or estimation process, and are therefore suboptimal. In this paper, we propose to learn probabilistic label maps using a generative model on given set of binary label maps. The proposed approach generalizes well on unseen data while simultaneously capturing the variability in the training samples. Efficiency of the proposed approach is demonstrated for consensus generation and shape-based clustering using synthetic datasets as well as left atrial segmentations from late-gadolinium enhancement MRI.
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http://dx.doi.org/10.1109/ISBI.2016.7493353DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5228593PMC
April 2016

Quantitative comparison of cortical bone thickness using correspondence-based shape modeling in patients with cam femoroacetabular impingement.

J Orthop Res 2017 08 8;35(8):1743-1753. Epub 2016 Nov 8.

Department of Bioengineering, University of Utah, Salt Lake City, Utah, 84112.

The proximal femur is abnormally shaped in patients with cam-type femoroacetabular impingement (FAI). Impingement may elicit bone remodeling at the proximal femur, causing increases in cortical bone thickness. We used correspondence-based shape modeling to quantify and compare cortical thickness between cam patients and controls for the location of the cam lesion and the proximal femur. Computed tomography images were segmented for 45 controls and 28 cam-type FAI patients. The segmentations were input to a correspondence-based shape model to identify the region of the cam lesion. Median cortical thickness data over the region of the cam lesion and the proximal femur were compared between mixed-gender and gender-specific groups. Median [interquartile range] thickness was significantly greater in FAI patients than controls in the cam lesion (1.47 [0.64] vs. 1.13 [0.22] mm, respectively; p < 0.001) and proximal femur (1.28 [0.30] vs. 0.97 [0.22] mm, respectively; p < 0.001). Maximum thickness in the region of the cam lesion was more anterior and less lateral (p < 0.001) in FAI patients. Male FAI patients had increased thickness compared to male controls in the cam lesion (1.47 [0.72] vs. 1.10 [0.19] mm, respectively; p < 0.001) and proximal femur (1.25 [0.29] vs. 0.94 [0.17] mm, respectively; p < 0.001). Thickness was not significantly different between male and female controls.

Clinical Significance: Studies of non-pathologic cadavers have provided guidelines regarding safe surgical resection depth for FAI patients. However, our results suggest impingement induces cortical thickening in cam patients, which may strengthen the proximal femur. Thus, these previously established guidelines may be too conservative. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:1743-1753, 2017.
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http://dx.doi.org/10.1002/jor.23468DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5407942PMC
August 2017

Entropy-based Correspondence Improvement of Interpolated Skeletal Models.

Comput Vis Image Underst 2016 Oct 21;151:72-79. Epub 2016 Sep 21.

University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Statistical analysis of shape representations relies on having good correspondence across a population. Improving correspondence yields improved statistics. Point distribution models (PDMs) are often used to represent object boundaries. Skeletal representations (s-reps) model object widths and boundary directions as well as boundary positions, so they should yield better correspondence. We present two methods: one for continuously interpolating a discretely-sampled skeletal model and one for improving correspondence by using this interpolation to shift skeletal samples to new positions. The interpolation operates by an extension of the mathematics of medial structures. As with Cates' boundary-based method, we evaluate correspondence in terms of regularity and shape-feature population entropies. Evaluation on both synthetic and real data shows that our method both improves correspondence of s-rep models fit to segmented lateral ventricles and that the combined boundary-and-skeletal PDMs implied by these optimized s-reps have better correspondence than optimized boundary PDMs.
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http://dx.doi.org/10.1016/j.cviu.2015.11.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6980525PMC
October 2016

Compressive Sensing Based Q-Space Resampling for Handling Fast Bulk Motion in Hardi Acquisitions.

Proc IEEE Int Symp Biomed Imaging 2016 16;2016:907-910. Epub 2016 Jun 16.

Tandon School of Engineering, Department of Computer Science & Engineering, NYU, USA.

Diffusion-weighted (DW) MRI has become a widely adopted imaging modality to reveal the underlying brain connectivity. Long acquisition times and/or non-cooperative patients increase the chances of motion-related artifacts. Whereas motion results in inter-gradient misalignment which can be handled via retrospective motion correction algorithms, motion usually affects data during the application of a single diffusion gradient causing signal dropout artifacts. Common practices opt to discard gradients bearing signal attenuation due to the difficulty of their retrospective correction, with the disadvantage to lose full gradients for further processing. Nonetheless, such attenuation might only affect limited number of slices within a gradient volume. Q-space resampling has recently been proposed to recover corrupted slices while saving gradients for subsequent reconstruction. However, few corrupted gradients are implicitly assumed which might not hold in case of scanning unsedated infants or patients in pain. In this paper, we propose to adopt recent advances in compressive sensing based reconstruction of the diffusion orientation distribution functions (ODF) with under sampled measurements to resample corrupted slices. We make use of Simple Harmonic Oscillator based Reconstruction and Estimation (SHORE) basis functions which can analytically model ODF from arbitrary sampled signals. We demonstrate the impact of the proposed resampling strategy compared to state-of-art resampling and gradient exclusion on simulated intra-gradient motion as well as samples from real DWI data.
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http://dx.doi.org/10.1109/ISBI.2016.7493412DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5826629PMC
June 2016

Subject-Motion Correction in HARDI Acquisitions: Choices and Consequences.

Front Neurol 2014 9;5:240. Epub 2014 Dec 9.

Scientific Computing and Imaging Institute , Salt Lake City, UT , USA.

Diffusion-weighted imaging (DWI) is known to be prone to artifacts related to motion originating from subject movement, cardiac pulsation, and breathing, but also to mechanical issues such as table vibrations. Given the necessity for rigorous quality control and motion correction, users are often left to use simple heuristics to select correction schemes, which involves simple qualitative viewing of the set of DWI data, or the selection of transformation parameter thresholds for detection of motion outliers. The scientific community offers strong theoretical and experimental work on noise reduction and orientation distribution function (ODF) reconstruction techniques for HARDI data, where post-acquisition motion correction is widely performed, e.g., using the open-source DTIprep software (1), FSL (the FMRIB Software Library) (2), or TORTOISE (3). Nonetheless, effects and consequences of the selection of motion correction schemes on the final analysis, and the eventual risk of introducing confounding factors when comparing populations, are much less known and far beyond simple intuitive guessing. Hence, standard users lack clear guidelines and recommendations in practical settings. This paper reports a comprehensive evaluation framework to systematically assess the outcome of different motion correction choices commonly used by the scientific community on different DWI-derived measures. We make use of human brain HARDI data from a well-controlled motion experiment to simulate various degrees of motion corruption and noise contamination. Choices for correction include exclusion/scrubbing or registration of motion corrupted directions with different choices of interpolation, as well as the option of interpolation of all directions. The comparative evaluation is based on a study of the impact of motion correction using four metrics that quantify (1) similarity of fiber orientation distribution functions (fODFs), (2) deviation of local fiber orientations, (3) global brain connectivity via graph diffusion distance (GDD), and (4) the reproducibility of prominent and anatomically defined fiber tracts. Effects of various motion correction choices are systematically explored and illustrated, leading to a general conclusion of discouraging users from setting ad hoc thresholds on the estimated motion parameters beyond which volumes are claimed to be corrupted.
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http://dx.doi.org/10.3389/fneur.2014.00240DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4260507PMC
December 2014

A PRELIMINARY STUDY ON THE EFFECT OF MOTION CORRECTION ON HARDI RECONSTRUCTION.

Proc IEEE Int Symp Biomed Imaging 2014 Apr;2014:1055-1058

Scientific Computing and Imaging Institute, Salt Lake City, UT, USA.

Post-acquisition motion correction is widely performed in diffusion-weighted imaging (DWI) to guarantee voxel-wise correspondence between DWIs. Whereas this is primarily motivated to save as many scans as possible if corrupted by motion, users do not fully understand the consequences of different types of interpolation schemes on the final analysis. Nonetheless, interpolation might increase the partial volume effect while not preserving the volume of the diffusion profile, whereas excluding poor DWIs may affect the ability to resolve crossing fibers especially with small separation angles. In this paper, we investigate the effect of interpolating diffusion measurements as well as the elimination of bad directions on the reconstructed fiber orientation diffusion functions and on the estimated fiber orientations. We demonstrate such an effect on synthetic and real HARDI datasets. Our experiments demonstrate that the effect of interpolation is more significant with small fibers separation angles where the exclusion of motion-corrupted directions decreases the ability to resolve such crossing fibers.
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http://dx.doi.org/10.1109/ISBI.2014.6868055DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4209744PMC
April 2014

Modeling of the lung nodules for detection in LDCT scans.

Annu Int Conf IEEE Eng Med Biol Soc 2010 ;2010:3618-21

Department of Electrical & Computer Eng., University of Louisville, USA.

A novel approach is proposed for generating data driven models of the lung nodules appearing in low dose CT (LDCT) scans of the human chest. Four types of common lung nodules are analyzed using Active Appearance Model methods to create descriptive lung nodule models. The proposed approach is also applicable for automatic classification of nodules into pathologies given a descriptive database. This approach is a major step forward for early diagnosis of lung cancer. We show the performance of the new nodule models on clinical datasets which illustrates significant improvements in both sensitivity and specificity.
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http://dx.doi.org/10.1109/IEMBS.2010.5627446DOI Listing
April 2011

Shape modeling of the corpus callosum.

Annu Int Conf IEEE Eng Med Biol Soc 2010 ;2010:4288-91

Department of Electrical and Computer Engineering, University of Louisville, USA.

A novel approach for shape modeling of the corpus callosum (cc) is introduced where the contours of the cc are extracted by image/volume segmentation, and a Bezier curve is used to connect the vertices of the sampled contours, generating a parametric polynomial representation. These polynomials are shown to maintain the characteristics of the original cc, thus are suitable for classification of populations. The Bernstein polynomials are used in fitting the Bezier curves. The coefficients of the Bernstein polynomials are shown to capture the geometric features of the cc, and are able to describe deformations. We use these coefficients, in conjunction with the Fourier Descriptors and other features, to discriminate between autistic and normal brains. The approach is tested on T1-weighted MRI scans of 16 normal and 22 autistic subjects and shows its ability to provide perfect classification, suggesting that the approach is worth investigating on a larger population with the hope of providing early identification and intervention of autism using neuroimaging.
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http://dx.doi.org/10.1109/IEMBS.2010.5626169DOI Listing
March 2011

Toward precise pulmonary nodule descriptors for nodule type classification.

Med Image Comput Comput Assist Interv 2010 ;13(Pt 3):626-33

Department of Electrical and Computer Engineering, University of Louisville Medical Imaging Division, Jewish Hospital, Louisville, KY, USA.

A framework for nodule feature-based extraction is presented to classify lung nodules in low-dose CT slices (LDCT) into four categories: juxta, well-circumscribed, vascularized and pleural-tail, based on the extracted information. The Scale Invariant Feature Transform (SIFT) and an adaptation to Daugman's Iris Recognition algorithm are used for analysis. The SIFT descriptor results are projected to lower-dimensional subspaces using PCA and LDA. Complex Gabor wavelet nodule response obtained from an adopted Daugman Iris Recognition algorithm revealed improvements from the original Daugman binary iris code. This showed that binarized nodule responses (codes) are inadequate for classification since nodules lack texture concentration as seen in the iris, while the SIFT algorithm projected using PCA showed robustness and precision in classification.
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http://dx.doi.org/10.1007/978-3-642-15711-0_78DOI Listing
November 2010
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