1,947 results match your criteria Computerized Medical Imaging and Graphics [Journal]


Patch-based lung ventilation estimation using multi-layer supervoxels.

Comput Med Imaging Graph 2019 Apr 5;74:49-60. Epub 2019 Apr 5.

Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK.

Patch-based approaches have received substantial attention over the recent years in medical imaging. One of their potential applications may be to provide more anatomically consistent ventilation maps estimated on dynamic lung CT. An assessment of regional lung function may act as a guide for radiotherapy, ensuring a more accurate treatment plan. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2019.04.002DOI Listing
April 2019
1 Read

Patch similarity in ultrasound images with hypothesis testing and stochastic distances.

Comput Med Imaging Graph 2019 Mar 24;74:37-48. Epub 2019 Mar 24.

Federal University of São Carlos, Washington Luís Highway, km 235, PO Box 676, São Carlos, Brazil; Centro Universitário Campo Limpo Paulista, Guatemala Street, 167, Campo Limpo Paulista, Brazil.

Patch-based techniques have been largely applied to process ultrasound (US) images, with applications in various fields as denoising, segmentation, and registration. An important aspect of the performance of these techniques is how to measure the similarity between patches. While it is usual to base the similarity on the Euclidean distance when processing images corrupted by additive Gaussian noise, finding measures suitable for the multiplicative nature of the speckle in US images is still an open research. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2019.03.001DOI Listing
March 2019
10 Reads

Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks.

Comput Med Imaging Graph 2019 Mar 22;74:25-36. Epub 2019 Mar 22.

Department of Electrical and Computer Engineering, University of Texas at El Paso, El Paso, TX, 79968, United States. Electronic address:

Deep learning techniques have been extensively used in computerized pulmonary nodule analysis in recent years. Many reported studies still utilized hybrid methods for diagnosis, in which convolutional neural networks (CNNs) are used only as one part of the pipeline, and the whole system still needs either traditional image processing modules or human intervention to obtain final results. In this paper, we introduced a fast and fully-automated end-to-end system that can efficiently segment precise lung nodule contours from raw thoracic CT scans. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2019.02.003DOI Listing
March 2019
2 Reads

Identification of the presence of ischaemic stroke lesions by means of texture analysis on brain magnetic resonance images.

Comput Med Imaging Graph 2019 Mar 16;74:12-24. Epub 2019 Mar 16.

Centre for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Valencia, Spain.

Background: The differential quantification of brain atrophy, white matter hyperintensities (WMH) and stroke lesions is important in studies of stroke and dementia. However, the presence of stroke lesions is usually overlooked by automatic neuroimage processing methods and the-state-of-the-art deep learning schemes, which lack sufficient annotated data. We explore the use of radiomics in identifying whether a brain magnetic resonance imaging (MRI) scan belongs to an individual that had a stroke or not. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2019.02.006DOI Listing
March 2019
1 Read

Special issue on machine learning in medical imaging.

Comput Med Imaging Graph 2019 Mar 16;74:10-11. Epub 2019 Mar 16.

School of Computing Science, Simon Fraser University, Canada.

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http://dx.doi.org/10.1016/j.compmedimag.2019.03.003DOI Listing
March 2019
1.496 Impact Factor

Computerized identification of the vasculature surrounding a pulmonary nodule.

Comput Med Imaging Graph 2019 Mar 16;74:1-9. Epub 2019 Mar 16.

Departments of Radiology and Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA. Electronic address:

Objectives: The idea of inferring the prognosis of lung tumor via its surrounding vasculature is novel, but not supported by available technology. In this study, we described and validated a computerized method to identify the vasculature surrounding a pulmonary nodule depicted on low-dose computed tomography (LDCT).

Materials And Methods: The proposed computerized scheme identified the vessels surrounding a lung nodule by using novel computational geometric solutions and quantified them by decomposing the vessels into independent vessel branches. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2019.03.002DOI Listing
March 2019
1 Read

3D convolutional neural networks for tumor segmentation using long-range 2D context.

Comput Med Imaging Graph 2019 Apr 21;73:60-72. Epub 2019 Feb 21.

Université Côte d'Azur, Inria Sophia Antipolis, France.

We present an efficient deep learning approach for the challenging task of tumor segmentation in multisequence MR images. In recent years, Convolutional Neural Networks (CNN) have achieved state-of-the-art performances in a large variety of recognition tasks in medical imaging. Because of the considerable computational cost of CNNs, large volumes such as MRI are typically processed by subvolumes, for instance slices (axial, coronal, sagittal) or small 3D patches. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2019.02.001DOI Listing
April 2019
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CT and 3D-ultrasound registration for spatial comparison of post-EVAR abdominal aortic aneurysm measurements: A cross-sectional study.

Comput Med Imaging Graph 2019 Apr 2;73:49-59. Epub 2019 Mar 2.

Department of Vascular Surgery, University Hospital of Copenhagen, Denmark.

Objective: The aim of the present study is to provide a methodology to register volumes of stented abdominal aortic aneurysm, imaged by 3D-US and CT modalities. After registration, the method enables to compare the spatial location of measurements and AAA size in a common coordinate system.

Methods: The study is cross-sectional and compares volumes acquired within a few days, in order to eliminate changes due to the evolution of AAA shape after treatment. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2019.02.004DOI Listing
April 2019
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ReTouchImg: Fusioning from-local-to-global context detection and graph data structures for fully-automatic specular reflection removal for endoscopic images.

Comput Med Imaging Graph 2019 Apr 4;73:39-48. Epub 2019 Mar 4.

Department of Computer Science, George Washington University, Washington DC, USA.

Minimally invasive surgical and diagnostic systems are commonly used in clinical practices. However, the accuracy and robustness of these systems depend heavily on computer based processes such as tracking, detecting or segmenting clinically meaningful regions of interest, which are significantly affected by the inherent specular reflections that appear on the organs' surfaces. Restoration of the acquired data for clinical purposes still presents challenges because of the high texture and color variations across the image. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2019.02.002DOI Listing

Novel feature-based visualization of the unsteady blood flow in intracranial aneurysms with the help of proper orthogonal decomposition (POD).

Authors:
Gábor Janiga

Comput Med Imaging Graph 2019 Apr 26;73:30-38. Epub 2019 Jan 26.

Lab. of Fluid Dynamics and Technical Flows, University of Magdeburg "Otto von Guericke", Universitätsplatz 2, Magdeburg D-39106, Germany. Electronic address:

The recognition and interpretation of pulsatile subject-specific blood flow is a challenging task. Animations of various quantities - such as blood flow velocity, pressure, or wall shear stress - can be depicted to visualize the complex time-varying flow features, normally in a region of interest. Traditional visualization methods however can hardly convey the dynamic information of the system. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2019.01.001DOI Listing

Novel lymph node segmentation and proliferation index measurement for skin melanoma biopsy images.

Comput Med Imaging Graph 2019 Apr 16;73:19-29. Epub 2019 Feb 16.

University of Alberta, Edmonton, AB, T6G 2V4, Canada. Electronic address:

The lymphatic system is the immune system of the human body, and includes networks of vessels spread over the body, lymph nodes, and lymph fluid. The lymph nodes are considered as purification units that collect the lymph fluid from the lymph vessels. Since the lymph nodes collect the cancer cells that escape from a malignant tumor and try to spread to the rest of the body, the lymph node analysis is important for staging many types skin and breast cancers. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2019.01.006DOI Listing
April 2019
4 Reads

A decision support tool for early detection of knee OsteoArthritis using X-ray imaging and machine learning: Data from the OsteoArthritis Initiative.

Comput Med Imaging Graph 2019 Apr 29;73:11-18. Epub 2019 Jan 29.

University of Orléans, I3MTO Laboratory, EA 4708, 45067 Orléans, France; Hospital of Orléans, Rheumatology Department, 45032 Orléans, France.

This paper presents a fully developed computer aided diagnosis (CAD) system for early knee OsteoArthritis (OA) detection using knee X-ray imaging and machine learning algorithms. The X-ray images are first preprocessed in the Fourier domain using a circular Fourier filter. Then, a novel normalization method based on predictive modeling using multivariate linear regression (MLR) is applied to the data in order to reduce the variability between OA and healthy subjects. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2019.01.007DOI Listing
April 2019
1 Read

A novel diagnostic information based framework for super-resolution of retinal fundus images.

Comput Med Imaging Graph 2019 Mar 1;72:22-33. Epub 2019 Feb 1.

Electro Medical and Speech Technology Lab, Indian Institute of Technology, Guwahati, India.

Advancements in tele-medicine have led to the development of portable and cheap hand-held retinal imaging devices. However, the images obtained from these devices have low resolution (LR) and poor quality that may not be suitable for retinal disease diagnosis. Therefore, this paper proposes a novel framework for the super-resolution (SR) of the LR fundus images. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2019.01.002DOI Listing
March 2019
1 Read

Layer-based visualization and biomedical information exploration of multi-channel large histological data.

Comput Med Imaging Graph 2019 Mar 2;72:34-46. Epub 2019 Feb 2.

Robarts Research Institute, Western University, 1151 Richmond St. N., London, Ontario, Canada N6A 5B7; Department of Medical Biophysics, Western University, London, Ontario, Canada N6A 5C1. Electronic address:

Background And Objective: Modern microscopes can acquire multi-channel large histological data from tissues of human beings or animals, which contain rich biomedical information for disease diagnosis and biological feature analysis. However, due to the large size, fuzzy tissue structure, and complicated multiple elements integrated in the image color space, it is still a challenge for current software systems to effectively calculate histological data, show the inner tissue structures and unveil hidden biomedical information. Therefore, we developed new algorithms and a software platform to address this issue. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2019.01.004DOI Listing
March 2019
1 Read
1.496 Impact Factor

Deep learning for cell image segmentation and ranking.

Comput Med Imaging Graph 2019 Mar 30;72:13-21. Epub 2019 Jan 30.

Federal University of Ceará, Brazil. Electronic address:

Ninety years after its invention, the Pap test continues to be the most used method for the early identification of cervical precancerous lesions. In this test, the cytopathologists look for microscopic abnormalities in and around the cells, which is a time-consuming and prone to human error task. This paper introduces computational tools for cytological analysis that incorporate cell segmentation deep learning techniques. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2019.01.003DOI Listing

RNN-based longitudinal analysis for diagnosis of Alzheimer's disease.

Comput Med Imaging Graph 2019 Apr 26;73:1-10. Epub 2019 Jan 26.

Department of Instrument Science and Engineering, School of EIEE, Shanghai Jiao Tong University, 200240 China.; Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, Shanghai Jiao Tong University, China. Electronic address:

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and other mental functions. Magnetic resonance images (MRI) have been widely used as an important imaging modality of brain for AD diagnosis and monitoring the disease progression. The longitudinal analysis of sequential MRIs is important to model and measure the progression of the disease along the time axis for more accurate diagnosis. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2019.01.005DOI Listing

Uncertainty-aware asynchronous scattered motion interpolation using Gaussian process regression.

Comput Med Imaging Graph 2019 Mar 21;72:1-12. Epub 2018 Dec 21.

Department of Mathematics and Computer Science, University of Bremen, Bremen, Germany; Fraunhofer Institute for Medical Image Computing MEVIS, Bremen, Germany; Department of Radiology, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA.

We address the problem of interpolating randomly non-uniformly spatiotemporally scattered uncertain motion measurements, which arises in the context of soft tissue motion estimation. Soft tissue motion estimation is of great interest in the field of image-guided soft-tissue intervention and surgery navigation, because it enables the registration of pre-interventional/pre-operative navigation information on deformable soft-tissue organs. To formally define the measurements as spatiotemporally scattered motion signal samples, we propose a novel motion field representation. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.12.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6433137PMC
March 2019
1 Read

Patch-based system for Classification of Breast Histology images using deep learning.

Comput Med Imaging Graph 2019 Jan 1;71:90-103. Epub 2018 Dec 1.

Department of Computer Science and Engineering, Jadavpur University, Kolkata-32, India. Electronic address:

In this work, we proposed a patch-based classifier (PBC) using Convolutional neural network (CNN) for automatic classification of histopathological breast images. Presence of limited images necessitated extraction of patches and augmentation to boost the number of training samples. Thus patches of suitable sizes carrying crucial diagnostic information were extracted from the original images. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S08956111183020
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http://dx.doi.org/10.1016/j.compmedimag.2018.11.003DOI Listing
January 2019
11 Reads

Patch spaces and fusion strategies in patch-based label fusion.

Comput Med Imaging Graph 2019 Jan 6;71:79-89. Epub 2018 Dec 6.

DTIC, Universitat Pompeu Fabra, Barcelona, Spain.

In the field of multi-atlas segmentation, patch-based approaches have shown promising results in the segmentation of biomedical images. In the most common approach, registration is used to warp the atlases to the target space and then the warped atlas labelmaps are fused into a consensus segmentation based on local appearance information encoded in form of patches. The registration step establishes spatial correspondence, which is important to obtain anatomical priors. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.11.004DOI Listing
January 2019
2 Reads

Predictive connectome subnetwork extraction with anatomical and connectivity priors.

Comput Med Imaging Graph 2019 Jan 25;71:67-78. Epub 2018 Aug 25.

Medical Image Analysis Lab, Simon Fraser University, Burnaby, BC, Canada.

We present a new method to identify anatomical subnetworks of the human connectome that are optimally predictive of targeted clinical variables, developmental outcomes or disease states. Given a training set of structural or functional brain networks, derived from diffusion MRI (dMRI) or functional MRI (fMRI) scans respectively, our sparse linear regression model extracts a weighted subnetwork. By enforcing novel backbone network and connectivity based priors along with a non-negativity constraint, the discovered subnetworks are simultaneously anatomically plausible, well connected, positively weighted and reasonably sparse. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.08.009DOI Listing
January 2019
4 Reads

Multi-level features combined end-to-end learning for automated pathological grading of breast cancer on digital mammograms.

Comput Med Imaging Graph 2019 Jan 13;71:58-66. Epub 2018 Nov 13.

National Digital Switching System Engineering & Technological R&D Center, China. Electronic address:

We propose to discriminate the pathological grades directly on digital mammograms instead of pathological images. An end-to-end learning algorithm based on the combined multi-level features is proposed. Low-level features are extracted and selected by supervised LASSO logistic regression. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.10.008DOI Listing
January 2019
15 Reads

Multi-sequence myocardium segmentation with cross-constrained shape and neural network-based initialization.

Comput Med Imaging Graph 2019 Jan 15;71:49-57. Epub 2018 Nov 15.

School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China. Electronic address:

For myocardial infarction (MI) patients, delayed enhancement (DE) and T2-weighted cardiovascular magnetic resonance imaging (CMR) can play significant roles in diagnosis, prognosis and therapeutic strategy evaluation. However, the non-rigid registration between different CMR sequences is particularly challenging and prevents the use of multi-sequence image analysis. In this article, we propose an approach for segmenting T2 and DE CMR simultaneously with cross-constrained shape and shape discrepancy compensation. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.11.001DOI Listing
January 2019
1 Read

CNN cascades for segmenting sparse objects in gigapixel whole slide images.

Comput Med Imaging Graph 2019 Jan 16;71:40-48. Epub 2018 Nov 16.

Institute of Imaging & Computer Vision, RWTH Aachen University, Aachen, Germany.

Due to the increasing availability of whole slide scanners facilitating digitization of histopathological tissue, large amounts of digital image data are being generated. Accordingly, there is a strong demand for the development of computer based image analysis systems. Here, we address application scenarios in histopathology consisting of sparse, small objects-of-interest occurring in the large gigapixel images. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.11.002DOI Listing
January 2019
15 Reads

Image super-resolution using progressive generative adversarial networks for medical image analysis.

Comput Med Imaging Graph 2019 Jan 16;71:30-39. Epub 2018 Nov 16.

IBM Research, Australia.

Anatomical landmark segmentation and pathology localisation are important steps in automated analysis of medical images. They are particularly challenging when the anatomy or pathology is small, as in retinal images (e.g. Read More

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January 2019
2 Reads

Fusing fine-tuned deep features for skin lesion classification.

Comput Med Imaging Graph 2019 Jan 3;71:19-29. Epub 2018 Nov 3.

School of Technology and Health, KTH Royal Institute of Technology, Stockholm, Sweden.

Malignant melanoma is one of the most aggressive forms of skin cancer. Early detection is important as it significantly improves survival rates. Consequently, accurate discrimination of malignant skin lesions from benign lesions such as seborrheic keratoses or benign nevi is crucial, while accurate computerised classification of skin lesion images is of great interest to support diagnosis. Read More

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January 2019
19 Reads

Patch-based classification of thyroid nodules in ultrasound images using direction independent features extracted by two-threshold binary decomposition.

Comput Med Imaging Graph 2019 Jan 31;71:9-18. Epub 2018 Oct 31.

Institute of Biophysics and Informatics, 1(st) Faculty of Medicine, Charles University, Salmovska 1, 120 00, Prague, Czech Republic; 3(rd) Department of Medicine, 1(st) Faculty of Medicine, Charles University and General University Hospital in Prague, U Nemocnice 1, 128 08, Praha 2, Czech Republic.

Ultrasound imaging of the thyroid gland is considered to be the best diagnostic choice for evaluating thyroid nodules in early stages, since it has been marked as cost-effective, non-invasive and risk-free. Computer aided diagnosis (CAD) systems can offer a second opinion to radiologists, thereby increasing the overall diagnostic accuracy of ultrasound imaging. Although current CAD systems exhibit promising results, their use in clinical practice is limited. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.10.001DOI Listing
January 2019
14 Reads
1.496 Impact Factor

Machine learning to predict lung nodule biopsy method using CT image features: A pilot study.

Comput Med Imaging Graph 2019 Jan 3;71:1-8. Epub 2018 Nov 3.

Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States. Electronic address:

Computed tomography (CT)-based screening on lung cancer mortality is poised to make lung nodule management a growing public health problem. Biopsy and pathologic analysis of suspicious nodules is necessary to ensure accurate diagnosis and appropriate intervention. Biopsy techniques vary as do the specialists that perform them and the ways lung nodule patients are referred and triaged. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S08956111183026
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http://dx.doi.org/10.1016/j.compmedimag.2018.10.006DOI Listing
January 2019
15 Reads

Denoising and spatial resolution enhancement of 4D flow MRI using proper orthogonal decomposition and lasso regularization.

Comput Med Imaging Graph 2018 Dec 7;70:165-172. Epub 2018 Aug 7.

Department of Mechanical Engineering, University of Wisconsin-Milwaukee, United States. Electronic address:

4D-Flow MRI has emerged as a powerful tool to non-invasively image blood velocity profiles in the human cardio-vascular system. However, it is plagued by issues such as velocity aliasing, phase offsets, acquisition noise, and low spatial and temporal resolution. In imaging small blood vessel malformations such as intra-cranial aneurysms, the spatial resolution of 4D-Flow is often inadequate to resolve fine flow features. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.07.003DOI Listing
December 2018
2 Reads

Evaluation of accuracy of automatic out-of-plane respiratory gating for DCEUS-based quantification using principal component analysis.

Comput Med Imaging Graph 2018 Dec 22;70:155-164. Epub 2018 Oct 22.

The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi' an Jiaotong University, Xi'an, PR China. Electronic address:

The accuracy of abdominal multi-parametric quantification based on dynamic contrast-enhanced ultrasound (DCEUS) is limited by out-of-plane severe distortion induced by respiratory motion. This study developed a fully automatic respiratory gating scheme by using principal component analysis to remove distortions and disturbances in free-breathing DCEUS-based quantification. Taking the known in-vitro perfusions as ground truths, we further evaluated the respiratory gating accuracy from multiple perspectives in a controllable rotary distortion flow model with out-of-plane severe distortion induced by respiratory motion. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.10.004DOI Listing
December 2018
1 Read
1.500 Impact Factor

Multiswarm heterogeneous binary PSO using win-win approach for improved feature selection in liver and kidney disease diagnosis.

Comput Med Imaging Graph 2018 Dec 17;70:135-154. Epub 2018 Oct 17.

Thoothukudi Medical College, Thoothukudi, India. Electronic address:

Feature selection is a significant preprocessing method in the classification part of an expert system. We propose a new Multiswarm Heterogeneous Binary Particle Swarm Optimization algorithm (MHBPSO) using a Win-Win approach to improve the performance of Binary Particle Swarm Optimization algorithm (BPSO) for feature selection. MHBPSO is a cooperation algorithm, which includes BPSO and its three variants such as Boolean PSO (BoPSO), Self Adjusted Hierarchical Boolean PSO (SAHBoPSO), and Catfish Self Adjusted Hierarchical Boolean PSO (CSAHBoPSO). Read More

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December 2018
16 Reads

Hybrid method combining superpixel, random walk and active contour model for fast and accurate liver segmentation.

Comput Med Imaging Graph 2018 Dec 15;70:119-134. Epub 2018 Sep 15.

Software College of Northeastern University, No. 195 Chuangxin Road, Shenyang, China. Electronic address:

Organ segmentation is an important pre-processing step in surgery planning and computer-aided diagnosis. In this paper, we propose a fast and accurate liver segmentation framework. Our proposed method combines a knowledge-based slice-by-slice Random Walk (RW) segmentation algorithm (proposed in our previous work) with a superpixel algorithm called the Contrast-enhanced Compact Watershed (CCWS) method to reduce computing time and memory costs. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.08.012DOI Listing
December 2018
23 Reads

Automatic localization of normal active organs in 3D PET scans.

Comput Med Imaging Graph 2018 Dec 29;70:111-118. Epub 2018 Sep 29.

Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Canada.

PET imaging captures the metabolic activity of tissues and is commonly visually interpreted by clinicians for detecting cancer, assessing tumor progression, and evaluating response to treatment. To automate accomplishing these tasks, it is important to distinguish between normal active organs and activity due to abnormal tumor growth. In this paper, we propose a deep learning method to localize and detect normal active organs visible in a 3D PET scan field-of-view. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.09.008DOI Listing
December 2018
1 Read

Alzheimer's disease diagnosis based on multiple cluster dense convolutional networks.

Comput Med Imaging Graph 2018 Dec 2;70:101-110. Epub 2018 Oct 2.

Department of Instrument Science and Engineering, School of EIEE, Shanghai Jiao Tong University, Shanghai, 200240, China; Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, Shanghai Jiao Tong University, Shanghai, 200240, China. Electronic address:

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and cognitive functions. Structural magnetic resonance images (MRI) play important role to evaluate the brain anatomical changes for AD Diagnosis. Machine learning technologies have been widely studied on MRI computation and analysis for quantitative evaluation and computer-aided-diagnosis of AD. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.09.009DOI Listing
December 2018
5 Reads

Survey of automated multiple sclerosis lesion segmentation techniques on magnetic resonance imaging.

Comput Med Imaging Graph 2018 Dec 5;70:83-100. Epub 2018 Oct 5.

Nuclear Medicine Department, Oncology Clinic 'ELPIDA', Children's Hospital 'A. Sofia', Goudi, Greece. Electronic address:

Multiple sclerosis (MS) is a chronic disease. It affects the central nervous system and its clinical manifestation can variate. Magnetic Resonance Imaging (MRI) is often used to detect, characterize and quantify MS lesions in the brain, due to the detailed structural information that it can provide. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.10.002DOI Listing
December 2018
2 Reads

An iterative multi-atlas patch-based approach for cortex segmentation from neonatal MRI.

Comput Med Imaging Graph 2018 Dec 22;70:73-82. Epub 2018 Sep 22.

IMT Atlantique, LaTIM U1101 INSERM, UBL, Brest, France.

Brain structure analysis in the newborn is a major health issue. This is especially the case for preterm neonates, in order to obtain predictive information related to the child development. In particular, the cortex is a structure of interest, that can be observed in magnetic resonance imaging (MRI). Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.09.003DOI Listing
December 2018
25 Reads

Multi-dimensional proprio-proximus machine learning for assessment of myocardial infarction.

Comput Med Imaging Graph 2018 Dec 26;70:63-72. Epub 2018 Sep 26.

Institute of High Performance Computing, A(⁎)STAR, Singapore. Electronic address:

This work presents a novel analysis methodology that utilises high-resolution, multi-dimensional information to better classify regions of the left ventricle after myocardial infarction. Specifically, the focus is to determine degree of infarction in regions of the left ventricle based on information extracted from cardiac magnetic resonance imaging. Enhanced classification accuracy is achieved using three mechanisms: Firstly, a plurality of indices/features is used in the pattern classification process, rather than a single index/feature (hence the term "multi-dimensional). Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.09.007DOI Listing
December 2018
20 Reads

SD-CNN: A shallow-deep CNN for improved breast cancer diagnosis.

Comput Med Imaging Graph 2018 Dec 22;70:53-62. Epub 2018 Sep 22.

Department of Radiology, Mayo Clinic in Arizona, Scottsdale, AZ, 85259, USA.

Breast cancer is the second leading cause of cancer death among women worldwide. Nevertheless, it is also one of the most treatable malignances if detected early. Screening for breast cancer with full field digital mammography (FFDM) has been widely used. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S08956111183023
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http://dx.doi.org/10.1016/j.compmedimag.2018.09.004DOI Listing
December 2018
21 Reads
1.500 Impact Factor

Deep learning nuclei detection: A simple approach can deliver state-of-the-art results.

Comput Med Imaging Graph 2018 Dec 17;70:43-52. Epub 2018 Sep 17.

Fraunhofer MEVIS, Am Fallturm 1, 28359, Bremen, Germany; Jacobs University, Campus Ring 1, 28759, Bremen, Germany. Electronic address:

Background: Deep convolutional neural networks have become a widespread tool for the detection of nuclei in histopathology images. Many implementations share a basic approach that includes generation of an intermediate map indicating the presence of a nucleus center, which we refer to as PMap. Nevertheless, these implementations often still differ in several parameters, resulting in different detection qualities. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.08.010DOI Listing
December 2018
2 Reads

Magnetic resonance angiography contrast enhancement and combined 3D visualization of cerebral vasculature and white matter pathways.

Comput Med Imaging Graph 2018 Dec 25;70:29-42. Epub 2018 Sep 25.

Department of Diagnostic and Interventional Neuroradiology, University Hospital Tübingen, Hoppe-Seyler-Strasse 3, D-72076 Tübingen, Germany.

Recently, in diffusion magnetic resonance imaging, the reconstruction and three-dimensional rendering of white matter pathways have been introduced to clinical routine protocols. In a number of clinical situations, for example the preoperative analysis of vascular pathologies, the assessment of spatial relations between vascular structures and nearby fiber pathways is of vital interest for treatment planning. In this paper, we present an approach to the integrated vessel and fiber visualization, based on a novel vascular contrast enhancement operator for Magnetic Resonance Angiography (MRA) datasets. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.09.005DOI Listing
December 2018
4 Reads

Adaptive fusion of texture-based grading for Alzheimer's disease classification.

Comput Med Imaging Graph 2018 Dec 7;70:8-16. Epub 2018 Sep 7.

Univ. Bordeaux, LaBRI, UMR 5800, PICTURA, F-33400 Talence, France; CNRS, LaBRI, UMR 5800, PICTURA, F-33400 Talence, France.

Alzheimer's disease is a neurodegenerative process leading to irreversible mental dysfunctions. To date, diagnosis is established after incurable brain structure alterations. The development of new biomarkers is crucial to perform an early detection of this disease. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.08.002DOI Listing
December 2018
3 Reads

3D imaging system for respiratory monitoring in pediatric intensive care environment.

Comput Med Imaging Graph 2018 Dec 25;70:17-28. Epub 2018 Sep 25.

CHU Sainte-Justine, Mother and Child University Hospital Center, Montréal, Canada.

Assessment of respiratory activity in pediatric intensive care unit allows a comprehensive view of the patient's condition. This allows the identification of high-risk cases for prompt and appropriate medical treatment. Numerous research works on respiration monitoring have been conducted in recent years. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S08956111183025
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http://dx.doi.org/10.1016/j.compmedimag.2018.09.006DOI Listing
December 2018
18 Reads

Practical guidelines for handling head and neck computed tomography artifacts for quantitative image analysis.

Comput Med Imaging Graph 2018 Nov 15;69:134-139. Epub 2018 Sep 15.

Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1420, Houston, Texas 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave., Houston, Texas 77030, United States; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1472, Houston, Texas 77030, United States.

Radiomics studies have demonstrated the potential use of quantitative image features to improve prognostic stratification of patients with head and neck cancer. Imaging protocol parameters that can affect radiomics feature values have been investigated, but the effects of artifacts caused by intrinsic patient factors have not. Two such artifacts that are common in patients with head and neck cancer are streak artifacts caused by dental fillings and beam-hardening artifacts caused by bone. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S08956111183017
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http://dx.doi.org/10.1016/j.compmedimag.2018.09.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6217839PMC
November 2018
6 Reads
1.500 Impact Factor

Semi-automatic lymphoma detection and segmentation using fully conditional random fields.

Comput Med Imaging Graph 2018 Dec 17;70:1-7. Epub 2018 Sep 17.

University of Rouen Normandy, LITIS EA 4108, 76183 Rouen, France. Electronic address:

The detection and delineation of the lymphoma volume are a critical step for its treatment and its outcome prediction. Positron Emission Tomography (PET) is widely used for lymphoma detection. Two common types of approaches can be distinguished for lymphoma detection and segmentation in PET. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S08956111183013
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http://dx.doi.org/10.1016/j.compmedimag.2018.09.001DOI Listing
December 2018
25 Reads

An EM-based semi-supervised deep learning approach for semantic segmentation of histopathological images from radical prostatectomies.

Comput Med Imaging Graph 2018 Nov 3;69:125-133. Epub 2018 Sep 3.

Department of Bioengineering, University of California, Los Angeles, CA, USA; Computational Integrated Diagnostics, Departments of Radiological Sciences and Pathology and Laboratory Medicine, University of California, Los Angeles, CA, USA. Electronic address:

Automated Gleason grading is an important preliminary step for quantitative histopathological feature extraction. Different from the traditional task of classifying small pre-selected homogeneous regions, semantic segmentation provides pixel-wise Gleason predictions across an entire slide. Deep learning-based segmentation models can automatically learn visual semantics from data, which alleviates the need for feature engineering. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.08.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6173982PMC
November 2018
1 Read

Optimal multi-object segmentation with novel gradient vector flow based shape priors.

Comput Med Imaging Graph 2018 Nov 30;69:96-111. Epub 2018 Aug 30.

Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA; Department of Radiation Oncology, The University of Iowa, Iowa City, IA 52242, USA. Electronic address:

Shape priors have been widely utilized in medical image segmentation to improve segmentation accuracy and robustness. A major way to encode such a prior shape model is to use a mesh representation, which is prone to causing self-intersection or mesh folding. Those problems require complex and expensive algorithms to mitigate. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.08.004DOI Listing
November 2018
2 Reads

Efficient multi-kernel multi-instance learning using weakly supervised and imbalanced data for diabetic retinopathy diagnosis.

Comput Med Imaging Graph 2018 Nov 25;69:112-124. Epub 2018 Aug 25.

Computing Science, University of Alberta, Edmonton, Alberta, Canada.

Objective: Diabetic retinopathy (DR) is one of the most serious complications of diabetes. Early detection and treatment of DR are key public health interventions that can significantly reduce the risk of vision loss. How to effectively screen and diagnose the retinal fundus image in order to identify retinopathy in time is a major challenge. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S08956111183047
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http://dx.doi.org/10.1016/j.compmedimag.2018.08.008DOI Listing
November 2018
5 Reads

Case-control comparison brain lesion segmentation for early infarct detection.

Comput Med Imaging Graph 2018 Nov 3;69:82-95. Epub 2018 Sep 3.

Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Victoria, Australia. Electronic address:

Computed Tomography (CT) images are widely used for the identification of abnormal brain tissues following infarct and hemorrhage of a stroke. The treatment of this medical condition mainly depends on doctors' experience. While manual lesion delineation by medical doctors is currently considered as the standard approach, it is time-consuming and dependent on each doctor's expertise and experience. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S08956111183024
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http://dx.doi.org/10.1016/j.compmedimag.2018.08.011DOI Listing
November 2018
11 Reads

A wavelet gradient sparsity based algorithm for reconstruction of reduced-view tomography datasets obtained with a monochromatic synchrotron-based X-ray source.

Comput Med Imaging Graph 2018 Nov 1;69:69-81. Epub 2018 Sep 1.

Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada.

High-resolution synchrotron computed tomography (CT) is very helpful in the diagnosis and monitor of chronic diseases including osteoporosis. Osteoporosis is characterized by low bone mass and cortical bone porosity best imaged with CT. Synchrotron CT requires a large number of angular projections to reconstruct images with high resolution for detailed and accurate diagnosis. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.08.005DOI Listing
November 2018
2 Reads

Computer-aided classification of prostate cancer grade groups from MRI images using texture features and stacked sparse autoencoder.

Comput Med Imaging Graph 2018 Nov 25;69:60-68. Epub 2018 Aug 25.

Department of Computer Science, Cochin University of Science and Technology, Kochi 682022, Kerala, India.

A novel method to determine the Grade Group (GG) in prostate cancer (PCa) using multi-parametric magnetic resonance imaging (mpMRI) biomarkers is investigated in this paper. In this method, high-level features are extracted from hand-crafted texture features using a deep network of stacked sparse autoencoders (SSAE) and classified them using a softmax classifier (SMC). Transaxial T2 Weighted (T2W), Apparent Diffusion Coefficient (ADC) and high B-Value Diffusion-Weighted (BVAL) images obtained from PROSTATEx-2 2017 challenge dataset are used in this technique. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.08.006DOI Listing
November 2018
13 Reads

Learning to combine complementary segmentation methods for fetal and 6-month infant brain MRI segmentation.

Comput Med Imaging Graph 2018 Nov 28;69:52-59. Epub 2018 Aug 28.

Universitat Pompeu Fabra, Dept. of Information and Communication Technologies, Tànger 122-140, 08018 Barcelona, Spain; ICREA, Pg. Lluis Companys 23, 08010 Barcelona, Spain.

Segmentation of brain structures during the pre-natal and early post-natal periods is the first step for subsequent analysis of brain development. Segmentation techniques can be roughly divided into two families. The first, which we denote as registration-based techniques, rely on initial estimates derived by registration to one (or several) templates. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.08.007DOI Listing
November 2018
2 Reads