1,936 results match your criteria Computerized Medical Imaging and Graphics [Journal]


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 Jan 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
January 2019

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

Comput Med Imaging Graph 2019 Feb 1. 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
February 2019
1 Read

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

Comput Med Imaging Graph 2019 Feb 2. 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
February 2019

Deep learning for cell image segmentation and ranking.

Comput Med Imaging Graph 2019 Jan 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
January 2019

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

Comput Med Imaging Graph 2019 Jan 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
January 2019

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

Comput Med Imaging Graph 2018 Dec 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
December 2018
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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http://dx.doi.org/10.1016/j.compmedimag.2018.11.003DOI Listing
January 2019
7 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
2 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
11 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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https://linkinghub.elsevier.com/retrieve/pii/S08956111183062
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http://dx.doi.org/10.1016/j.compmedimag.2018.11.002DOI Listing
January 2019
12 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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https://linkinghub.elsevier.com/retrieve/pii/S08956111183058
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http://dx.doi.org/10.1016/j.compmedimag.2018.10.005DOI Listing
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
16 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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https://linkinghub.elsevier.com/retrieve/pii/S08956111183019
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http://dx.doi.org/10.1016/j.compmedimag.2018.10.001DOI Listing
January 2019
8 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
10 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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https://linkinghub.elsevier.com/retrieve/pii/S08956111183025
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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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https://linkinghub.elsevier.com/retrieve/pii/S08956111183057
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http://dx.doi.org/10.1016/j.compmedimag.2018.10.003DOI Listing
December 2018
12 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
15 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
4 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
19 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
13 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
16 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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December 2018
14 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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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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http://dx.doi.org/10.1016/j.compmedimag.2018.09.001DOI Listing
December 2018
21 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
4 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
9 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
10 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
1 Read

MRI white matter lesion segmentation using an ensemble of neural networks and overcomplete patch-based voting.

Comput Med Imaging Graph 2018 Nov 3;69:43-51. Epub 2018 May 3.

Australian e-Health Research Centre, CSIRO, Brisbane, QLD, 4029, Australia.

Accurate quantification of white matter hyperintensities (WMH) from Magnetic Resonance Imaging (MRI) is a valuable tool for the analysis of normal brain ageing or neurodegeneration. Reliable automatic extraction of WMH lesions is challenging due to their heterogeneous spatial occurrence, their small size and their diffuse nature. In this paper, we present an automatic method to segment these lesions based on an ensemble of overcomplete patch-based neural networks. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.05.001DOI Listing
November 2018
1 Read

Automatic polyp frame screening using patch based combined feature and dictionary learning.

Comput Med Imaging Graph 2018 Nov 22;69:33-42. Epub 2018 Aug 22.

Intervention Centre, Oslo University Hospital, Oslo NO-0027, Norway; Institute of Clinical Medicine, University of Oslo, and the Norwegian University of Science and Technology (NTNU), Norway. Electronic address:

Polyps in the colon can potentially become malignant cancer tissues where early detection and removal lead to high survival rate. Certain types of polyps can be difficult to detect even for highly trained physicians. Inspired by aforementioned problem our study aims to improve the human detection performance by developing an automatic polyp screening framework as a decision support tool. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.08.001DOI Listing
November 2018
8 Reads

Intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks.

Comput Med Imaging Graph 2018 Nov 6;69:21-32. Epub 2018 Jul 6.

Yonsei University, 50 Yonsei-ro, Sinchon-dong, Seodaemun-gu, Seoul, South Korea.

Assessing the surgical margin during breast lumpectomy operations can avoid the need for additional surgery. Optical coherence tomography (OCT) is an imaging technique that has been proven to be efficient for this purpose. However, to avoid overloading the surgeon during the operation, automatic cancer detection at the surface of the removed tissue is needed. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.06.002DOI Listing
November 2018
2 Reads

Optimization of macaque brain DMRI connectome by neuron tracing and myelin stain data.

Comput Med Imaging Graph 2018 Nov 25;69:9-20. Epub 2018 Jun 25.

Cortical Architecture Imaging and Discovery Lab, The University of Georgia, Athens, GA, United States. Electronic address:

Accurate assessment of connectional anatomy of primate brains can be an important avenue to better understand the structural and functional organization of brains. To this end, numerous connectome projects have been initiated to create a comprehensive map of the connectional anatomy over a large spatial expanse. Tractography based on diffusion MRI (dMRI) data has been used as a tool by many connectome projects in that it is widely used to visualize axonal pathways and reveal microstructural features on living brains. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.06.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6176488PMC
November 2018
16 Reads

Cerebrovascular segmentation of TOF-MRA based on seed point detection and multiple-feature fusion.

Comput Med Imaging Graph 2018 Nov 1;69:1-8. Epub 2018 Aug 1.

Department of Biomedical Engineering, School of Medicine, Tsinghua University, Room C249, Beijing, 100084, China. Electronic address:

The accurate extraction of cerebrovascular structures from time-of-flight (TOF) data is important for diagnosis of cerebrovascular diseases and planning and navigation of neurosurgery. In this study, we proposed a cerebrovascular segmentation method based on automatic seed point detection and vascular multiple-feature fusion. First, the brain mask in the T1-MR image is detected to enable the extraction of the TOF brain structure by simultaneously acquiring the TOF image and its corresponding T1-MRI. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S08956111183016
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http://dx.doi.org/10.1016/j.compmedimag.2018.07.002DOI Listing
November 2018
13 Reads

3-D Neural denoising for low-dose Coronary CT Angiography (CCTA).

Comput Med Imaging Graph 2018 Dec 29;70:185-191. Epub 2018 Jul 29.

Diagnostic Imaging Institute, Sheba Medical Center, Affiliated with Sackler School of Medicine, Tel Aviv University, Israel. Electronic address:

CCTA has become an important tool for coronary arteries assessment in low and medium risk patients. However, it exposes the patient to significant radiation doses, resulting from high image quality requirements and acquisitions at multiple cardiac phases. For widespread use of CCTA for coronary assessment, significant reduction of radiation exposure with minimal image quality loss is still needed. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.07.004DOI Listing
December 2018
3 Reads

An effective teeth recognition method using label tree with cascade network structure.

Comput Med Imaging Graph 2018 Sep 17;68:61-70. Epub 2018 Jul 17.

Center of Digital Dentistry, Peking University School and Hospital of Stomatology & National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing, China.

In this article, we apply the deep learning technique to medical field for the teeth detection and classification of dental periapical radiographs, which is important for the medical curing and postmortem identification. We detect teeth in an input X-ray image and distinguish them from different position. An adult usually has 32 teeth, and some of them are similar while others have very different shape. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.07.001DOI Listing
September 2018
1 Read

Contrast-enhanced transthoracic echocardiography applied in evaluation of pulmonary right-to-left shunt: A preliminary study.

Comput Med Imaging Graph 2018 Sep 27;68:55-60. Epub 2018 Apr 27.

Department of Ultrasound, The Third People's Hospital of Shenzhen, Shenzhen, 518116, Guangdong, China. Electronic address:

Objective: To investigate the detection rate of patent foramen ovale-right to left shunt (PFO-RLS) and/or pulmonary-right to left shunt (P-RLS) via contrast-enhanced transthoracic echocardiography (c-TTE) in healthy participants, patients suffering from cryptogenic stroke and migraine with aura.

Methods: Initially, 20 healthy volunteers, 21 cases with cryptogenic stroke, and 18 cases with migraine aura were randomly selected, and all of them received c-TTE and transesophageal echocardiography (TEE) examinations. First of all, 0. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S08956111183007
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http://dx.doi.org/10.1016/j.compmedimag.2018.04.007DOI Listing
September 2018
10 Reads

Automatic histologically-closer classification of skin lesions.

Comput Med Imaging Graph 2018 Sep 4;68:40-54. Epub 2018 Jun 4.

Programa de Pós-Graduação em Informática Aplicada, Universidade de Fortaleza, Fortaleza, Ceará, Brazil. Electronic address:

According to the American Cancer Society, melanoma is one of the most common types of cancer in the world. In 2017, approximately 87,110 new cases of skin cancer were diagnosed in the United States alone. A dermatoscope is a tool that captures lesion images with high resolution and is one of the main clinical tools to diagnose, evaluate and monitor this disease. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.05.004DOI Listing
September 2018
3 Reads

Combined radiogrammetry and texture analysis for early diagnosis of osteoporosis using Indian and Swiss data.

Comput Med Imaging Graph 2018 Sep 26;68:25-39. Epub 2018 May 26.

Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, Karnataka, India. Electronic address:

Osteoporosis is a bone disorder characterized by bone loss and decreased bone strength. The most widely used technique for detection of osteoporosis is the measurement of bone mineral density (BMD) using dual energy X-ray absorptiometry (DXA). But DXA scans are expensive and not widely available in low-income economies. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.05.003DOI Listing
September 2018
7 Reads
1.500 Impact Factor

Fast anatomy segmentation by combining coarse scale multi-atlas label fusion with fine scale corrective learning.

Comput Med Imaging Graph 2018 Sep 24;68:16-24. Epub 2018 May 24.

IBM Almaden Research Center, 650 Harry Rd, San Jose, CA 94120, United States.

Deformable registration based multi-atlas segmentation has been successfully applied in a broad range of anatomy segmentation applications. However, the excellent performance comes with a high computational burden due to the requirement for deformable image registration and voxel-wise label fusion. To address this problem, we investigate the role of corrective learning (Wang et al. Read More

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http://dx.doi.org/10.1016/j.compmedimag.2018.05.002DOI Listing
September 2018
3 Reads