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Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society

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https://read.qxmd.com/read/30772074/layer-based-visualization-and-biomedical-information-exploration-of-multi-channel-large-histological-data
#1
Qi Zhang, Terry Peters, Aaron Fenster
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...
February 2, 2019: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/30772075/a-novel-diagnostic-information-based-framework-for-super-resolution-of-retinal-fundus-images
#2
Vineeta Das, Samarendra Dandapat, Prabin Kumar Bora
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. The method takes into consideration the diagnostic information in the fundus images during the SR process. In this work, SR is performed on the zone of interest of the fundus images...
February 1, 2019: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/30763802/deep-learning-for-cell-image-segmentation-and-ranking
#3
Flávio H D Araújo, Romuere R V Silva, Daniela M Ushizima, Mariana T Rezende, Cláudia M Carneiro, Andrea G Campos Bianchi, Fátima N S Medeiros
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. These techniques are capable of processing both free-lying and clumps of abnormal cells with a high overlapping rate from digitized images of conventional Pap smears...
January 30, 2019: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/30784984/a-decision-support-tool-for-early-detection-of-knee-osteoarthritis-using-x-ray-imaging-and-machine-learning-data-from-the-osteoarthritis-initiative
#4
Abdelbasset Brahim, Rachid Jennane, Rabia Riad, Thomas Janvier, Laila Khedher, Hechmi Toumi, Eric Lespessailles
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. At the feature selection/extraction stage, an independent component analysis (ICA) approach is used in order to reduce the dimensionality...
January 29, 2019: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/30763637/rnn-based-longitudinal-analysis-for-diagnosis-of-alzheimer-s-disease
#5
Ruoxuan Cui, Manhua Liu
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. Most existing methods extracted the features capturing the morphological abnormalities of brain and their longitudinal changes using MRIs and then designed a classifier to discriminate different groups...
January 26, 2019: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/30654093/uncertainty-aware-asynchronous-scattered-motion-interpolation-using-gaussian-process-regression
#6
Bojan Kocev, Horst Karl Hahn, Lars Linsen, William M Wells, Ron Kikinis
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...
December 21, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/30553173/patch-spaces-and-fusion-strategies-in-patch-based-label-fusion
#7
Oualid M Benkarim, Gemma Piella, Nadine Hahner, Elisenda Eixarch, Miguel Angel González Ballester, Gerard Sanroma
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. Patch-based label fusion in the target space has shown to produce very accurate segmentations although at the expense of registering all atlases to each target image...
December 6, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/30594745/patch-based-system-for-classification-of-breast-histology-images-using-deep-learning
#8
Kaushiki Roy, Debapriya Banik, Debotosh Bhattacharjee, Mita Nasipuri
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. The proposed classification system works in two different modes: one patch in one decision (OPOD) and all patches in one decision (APOD)...
December 1, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/30472409/cnn-cascades-for-segmenting-sparse-objects-in-gigapixel-whole-slide-images
#9
Michael Gadermayr, Ann-Kathrin Dombrowski, Barbara Mara Klinkhammer, Peter Boor, Dorit Merhof
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. To tackle the thereby arising challenges, we propose two different CNN cascade approaches which are subsequently applied to segment the glomeruli in whole slide images of the kidney and compared with conventional fully-convolutional networks...
November 16, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/30472408/image-super-resolution-using-progressive-generative-adversarial-networks-for-medical-image-analysis
#10
Dwarikanath Mahapatra, Behzad Bozorgtabar, Rahil Garnavi
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. vasculature branches or microaneurysm lesions) and cardiac MRI, or when the image is of low quality due to device acquisition parameters as in magnetic resonance (MR) scanners. We propose an image super-resolution method using progressive generative adversarial networks (P-GANs) that can take as input a low-resolution image and generate a high resolution image of desired scaling factor...
November 16, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/30500758/multi-sequence-myocardium-segmentation-with-cross-constrained-shape-and-neural-network-based-initialization
#11
Jie Liu, Hongzhi Xie, Shuyang Zhang, Lixu Gu
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...
November 15, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/30504094/multi-level-features-combined-end-to-end-learning-for-automated-pathological-grading-of-breast-cancer-on-digital-mammograms
#12
Jinjin Hai, Hongna Tan, Jian Chen, Minghui Wu, Kai Qiao, Jingbo Xu, Lei Zeng, Fei Gao, Dapeng Shi, Bin Yan
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. Convolutional Neural Network (CNN) is designed to extract high-level semantic features. These extracted multi-level features are combined to optimize the new CNN end to end to make different parts of the network learn to pay attention to different level of features...
November 13, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/30458354/fusing-fine-tuned-deep-features-for-skin-lesion-classification
#13
Amirreza Mahbod, Gerald Schaefer, Isabella Ellinger, Rupert Ecker, Alain Pitiot, Chunliang Wang
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. In this paper, we propose a fully automatic computerised method to classify skin lesions from dermoscopic images. Our approach is based on a novel ensemble scheme for convolutional neural networks (CNNs) that combines intra-architecture and inter-architecture network fusion...
November 3, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/30448741/machine-learning-to-predict-lung-nodule-biopsy-method-using-ct-image-features-a-pilot-study
#14
Yohan Sumathipala, Majid Shafiq, Erika Bongen, Connor Brinton, David Paik
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. The largest dichotomy is between minimally invasive biopsy (MIB) and surgical biopsy (SB). Cases of unsuccessful MIB preceding a SB can result in considerable delay in definitive care with potentially an adverse impact on prognosis besides potentially avoidable healthcare expenditures...
November 3, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/30268005/practical-guidelines-for-handling-head-and-neck-computed-tomography-artifacts-for-quantitative-image-analysis
#15
Rachel B Ger, Daniel F Craft, Dennis S Mackin, Shouhao Zhou, Rick R Layman, A Kyle Jones, Hesham Elhalawani, Clifton D Fuller, Rebecca M Howell, Heng Li, R Jason Stafford, Laurence E Court
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. The purpose of this study was to test the impact of these artifacts and if needed, methods for compensating for these artifacts in head and neck radiomics studies...
November 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/30243216/an-em-based-semi-supervised-deep-learning-approach-for-semantic-segmentation-of-histopathological-images-from-radical-prostatectomies
#16
Jiayun Li, William Speier, King Chung Ho, Karthik V Sarma, Arkadiusz Gertych, Beatrice S Knudsen, Corey W Arnold
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. However, performance of deep learning models is limited by the scarcity of large-scale fully annotated datasets, which can be both expensive and time-consuming to create...
November 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/30237146/optimal-multi-object-segmentation-with-novel-gradient-vector-flow-based-shape-priors
#17
Junjie Bai, Abhay Shah, Xiaodong Wu
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. In this paper, we propose a novel shape prior directly embedded in the voxel grid space, based on gradient vector flows of a pre-segmentation. The flexible and powerful prior shape representation is ready to be extended to simultaneously segmenting multiple interacting objects with minimum separation distance constraint...
November 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/30237145/efficient-multi-kernel-multi-instance-learning-using-weakly-supervised-and-imbalanced-data-for-diabetic-retinopathy-diagnosis
#18
Peng Cao, Fulong Ren, Chao Wan, Jinzhu Yang, Osmar Zaiane
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. In the traditional DR screening system, the accuracy of micro-aneurysm (MA) and hemorrhagic (H) lesion detection determines the final screening performance...
November 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/30219737/case-control-comparison-brain-lesion-segmentation-for-early-infarct-detection
#19
Fung Fung Ting, Kok Swee Sim, Chee Peng Lim
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. In this study, a case-control comparison brain lesion segmentation (CCBLS) method is proposed to segment the region pertaining to brain injury by comparing the voxel intensity of CT images between control subjects and stroke patients...
November 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/30212736/a-wavelet-gradient-sparsity-based-algorithm-for-reconstruction-of-reduced-view-tomography-datasets-obtained-with-a-monochromatic-synchrotron-based-x-ray-source
#20
S Ali Melli, Khan A Wahid, Paul Babyn, David M L Cooper, Ahmed M Hasan
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. However, this poses great risks and challenges for serial in-vivo human and animal imaging due to a large amount of X-ray radiation dose required that can damage living specimens...
November 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
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