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

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https://www.readbyqxmd.com/read/29890404/automatic-histologically-closer-classification-of-skin-lesions
#1
Pedro Pedrosa Rebouças Filho, Solon Alves Peixoto, Raul Victor Medeiros da Nóbrega, D Jude Hemanth, Aldisio Gonçalves Medeiros, Arun Kumar Sangaiah, Victor Hugo C de Albuquerque
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. This paper presents a new approach to classify melanoma automatically using structural co-occurrence matrix (SCM) of main frequencies extracted from dermoscopy images...
June 4, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29885566/combined-radiogrammetry-and-texture-analysis-for-early-diagnosis-of-osteoporosis-using-indian-and-swiss-data
#2
Anu Shaju Areeckal, Jagannath Kamath, Sophie Zawadynski, Michel Kocher, Sumam David S
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. In this paper, we propose a low cost pre-screening tool for the detection of low bone mass, using cortical radiogrammetry of third metacarpal bone and trabecular texture analysis of distal radius from hand and wrist radiographs...
May 26, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29870822/fast-anatomy-segmentation-by-combining-coarse-scale-multi-atlas-label-fusion-with-fine-scale-corrective-learning
#3
Hongzhi Wang, Deepika Kakrania, Hui Tang, Prasanth Prasanna, Tanveer Syeda-Mahmood
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., 2011) in speeding up multi-atlas segmentation. We propose to combine multi-atlas segmentation with corrective learning in a multi-scale analysis fashion for faster speeds...
May 24, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29775951/retinal-blood-vessel-segmentation-using-fully-convolutional-network-with-transfer-learning
#4
Zhexin Jiang, Hao Zhang, Yi Wang, Seok-Bum Ko
Since the retinal blood vessel has been acknowledged as an indispensable element in both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of the retinal vessel tree has become the prerequisite step for automated or computer-aided diagnosis systems. In this paper, a supervised method is presented based on a pre-trained fully convolutional network through transfer learning. This proposed method has simplified the typical retinal vessel segmentation problem from full-size image segmentation to regional vessel element recognition and result merging...
April 26, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29800886/cerebral-activation-effects-of-acupuncture-at-yanglinquan-gb34-point-acquired-using-resting-state-fmri
#5
Liansheng Liu, Shuqi Chen, Daohui Zeng, Hengguo Li, Changzheng Shi, Lihong Zhang
OBJECTIVE: To explore the central mechanism of acupuncture points for regional homogeneity(ReHo) of resting state in brain function after acupuncture at GB34. METHODS: Ten healthy volunteers were enrolled, which included 4 males and 6 females, aged 20-34 years old with median age of 23. The GE Signa HDxt 3.0 T magnetic resonance imaging were performed before (control group) and after acupuncture at GB34, and differences of different brain ReHo of 2 groups by statistical parametric mapping (SPM8) software and ReHo data processing methods were analyzed...
July 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29753964/a-web-based-system-for-neural-network-based-classification-in-temporomandibular-joint-osteoarthritis
#6
Priscille de Dumast, Clément Mirabel, Lucia Cevidanes, Antonio Ruellas, Marilia Yatabe, Marcos Ioshida, Nina Tubau Ribera, Loic Michoud, Liliane Gomes, Chao Huang, Hongtu Zhu, Luciana Muniz, Brandon Shoukri, Beatriz Paniagua, Martin Styner, Steve Pieper, Francois Budin, Jean-Baptiste Vimort, Laura Pascal, Juan Carlos Prieto
OBJECTIVE: The purpose of this study is to describe the methodological innovations of a web-based system for storage, integration and computation of biomedical data, using a training imaging dataset to remotely compute a deep neural network classifier of temporomandibular joint osteoarthritis (TMJOA). METHODS: This study imaging dataset consisted of three-dimensional (3D) surface meshes of mandibular condyles constructed from cone beam computed tomography (CBCT) scans...
July 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29753963/application-of-semi-automated-ultrasonography-on-nutritional-support-for-severe-acute-pancreatitis
#7
Ying Li, Yu Ye, Mei Yang, Haiying Ruan, Yuan Yu
OBJECTIVE: To evaluate the application value of semi-automated ultrasound on the guidance of nasogastrojejunal tube replacement for patients with acute severe pancreatitis (ASP), as well as the value of the nutritional support for standardized treatment in clinical practice. METHODS: The retrospective research was performed in our hospital, and 34 patients suffering from ASP were enrolled into this study. All these identified participants ever received CT scans in order to make definitive diagnoses...
July 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29738914/g4dari-geant4-gate-based-monte-carlo-simulation-interface-for-dosimetry-calculation-in-radiotherapy
#8
Faiçal A A Slimani, Mahdjoub Hamdi, M'hamed Bentourkia
Monte Carlo (MC) simulation is widely recognized as an important technique to study the physics of particle interactions in nuclear medicine and radiation therapy. There are different codes dedicated to dosimetry applications and widely used today in research or in clinical application, such as MCNP, EGSnrc and Geant4. However, such codes made the physics easier but the programming remains a tedious task even for physicists familiar with computer programming. In this paper we report the development of a new interface GEANT4 Dose And Radiation Interactions (G4DARI) based on GEANT4 for absorbed dose calculation and for particle tracking in humans, small animals and complex phantoms...
July 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29702348/exploring-diagnosis-and-imaging-biomarkers-of-parkinson-s-disease-via-iterative-canonical-correlation-analysis-based-feature-selection
#9
Luyan Liu, Qian Wang, Ehsan Adeli, Lichi Zhang, Han Zhang, Dinggang Shen
Parkinson's disease (PD) is a neurodegenerative disorder that progressively hampers the brain functions and leads to various movement and non-motor symptoms. However, it is difficult to attain early-stage PD diagnosis based on the subjective judgment of physicians in clinical routines. Therefore, automatic and accurate diagnosis of PD is highly demanded, so that the corresponding treatment can be implemented more appropriately. In this paper, we focus on finding the most discriminative features from different brain regions in PD through T1-weighted MR images, which can help the subsequent PD diagnosis...
July 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29684663/predictive-features-for-early-cancer-detection-in-barrett-s-esophagus-using-volumetric-laser-endomicroscopy
#10
Fons van der Sommen, Sander R Klomp, Anne-Fré Swager, Svitlana Zinger, Wouter L Curvers, Jacques J G H M Bergman, Erik J Schoon, Peter H N de With
The incidence of Barrett cancer is increasing rapidly and current screening protocols often miss the disease at an early, treatable stage. Volumetric Laser Endomicroscopy (VLE) is a promising new tool for finding this type of cancer early, capturing a full circumferential scan of Barrett's Esophagus (BE), up to 3-mm depth. However, the interpretation of these VLE scans can be complicated, due to the large amount of cross-sectional images and the subtle grayscale variations. Therefore, algorithms for automated analysis of VLE data can offer a valuable contribution to its overall interpretation...
July 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29660595/computer-assisted-subtyping-and-prognosis-for-non-small-cell-lung-cancer-patients-with-unresectable-tumor
#11
Maliazurina Saad, Tae-Sun Choi
BACKGROUND: The histological classification or subtyping of non-small cell lung cancer is essential for systematic therapy decisions. Differentiating between the two main subtypes of pulmonary adenocarcinoma and squamous cell carcinoma highlights the considerable differences that exist in the prognosis of patient outcomes. Physicians rely on a pathological analysis to reveal these phenotypic variations that requires invasive methods, such as biopsy and resection sample, but almost 70% of tumors are unresectable at the point of diagnosis...
July 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29609039/computer-aided-diagnosis-of-cavernous-malformations-in-brain-mr-images
#12
Huiquan Wang, S Nizam Ahmed, Mrinal Mandal
Cavernous malformation or cavernoma is one of the most common epileptogenic lesions. It is a type of brain vessel abnormality that can cause serious symptoms such as seizures, intracerebral hemorrhage, and various neurological disorders. Manual detection of cavernomas by physicians in a large set of brain MRI slices is a time-consuming and labor-intensive task and often delays diagnosis. In this paper, we propose a computer-aided diagnosis (CAD) system for cavernomas based on T2-weighted axial plane MRI image analysis...
June 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29602022/modeling-alzheimer-s-disease-cognitive-scores-using-multi-task-sparse-group-lasso
#13
Xiaoli Liu, André R Goncalves, Peng Cao, Dazhe Zhao, Arindam Banerjee
Alzheimer's disease (AD) is a severe neurodegenerative disorder characterized by loss of memory and reduction in cognitive functions due to progressive degeneration of neurons and their connections, eventually leading to death. In this paper, we consider the problem of simultaneously predicting several different cognitive scores associated with categorizing subjects as normal, mild cognitive impairment (MCI), or Alzheimer's disease (AD) in a multi-task learning framework using features extracted from brain images obtained from ADNI (Alzheimer's Disease Neuroimaging Initiative)...
June 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29573583/an-application-of-cascaded-3d-fully-convolutional-networks-for-medical-image-segmentation
#14
Holger R Roth, Hirohisa Oda, Xiangrong Zhou, Natsuki Shimizu, Ying Yang, Yuichiro Hayashi, Masahiro Oda, Michitaka Fujiwara, Kazunari Misawa, Kensaku Mori
Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of volumetric images. In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical structures (ranging from the large organs to thin vessels) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training class-specific models. To this end, we propose a two-stage, coarse-to-fine approach that will first use a 3D FCN to roughly define a candidate region, which will then be used as input to a second 3D FCN...
June 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29573582/deformable-respiratory-motion-correction-for-hepatic-rotational-angiography
#15
Alexandra Klugmann, Bastian Bier, Kerstin Müller, Andreas Maier, Mathias Unberath
Cone-beam rotational angiography enables 3D imaging of the hepatic vasculature and is considered beneficial for guidance of transcatheter arterial chemoembolization procedures. Respiratory motion during the rotational acquisition challenges state-of-the-art reconstruction algorithms as intra-scan motion leads to inconsistencies causing substantial blurring and streaking artifacts in uncompensated reconstructions, suggesting the need for motion correction. We propose an automated method for respiratory motion estimation and compensation based on registration of an initial 3D arterial model to vesselness enhanced 2D projection images...
June 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29573581/automatic-segmentation-of-pigment-deposits-in-retinal-fundus-images-of-retinitis-pigmentosa
#16
Nadia Brancati, Maria Frucci, Diego Gragnaniello, Daniel Riccio, Valentina Di Iorio, Luigi Di Perna
Retinitis Pigmentosa is an eye disease that presents with a slow loss of vision and then evolves until blindness results. The automatic detection of the early signs of retinitis pigmentosa acts as a great support to ophthalmologists in the diagnosis and monitoring of the disease in order to slow down the degenerative process. A large body of literature is devoted to the analysis of Retinitis Pigmentosa. However, all the existing approaches work on Optical Coherence Tomography (OCT) data, while hardly any attempts have been made working on fundus images...
June 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29567561/medical-image-analysis-of-knee-joint-lipoma-arborescens-and-arthroscopic-treatment
#17
Guangyu Zhu, Xiangdong Tian, Dongfeng Du, Ming Lei, Lei Guan, Jian Wang, Yetong Tan, Chen Yang, Xinxin Zheng
OBJECTIVE: Arthroscopy is a minimally invasive surgical procedure on a joint in which examination and treatment of knee damage is performed using a surgical device known as the arthroscope. Lipoma arborescens (LA), an infrequent intra-articular lesion, originates from mature adipose cells under subsynovial tissue. The synovial membrane is pale yellow with large villous projections. It is caused by various underlying factors. We found many patients with LA and processed them appropriately...
June 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29544118/automated-retinal-nerve-fiber-layer-defect-detection-using-fundus-imaging-in-glaucoma
#18
Rashmi Panda, N B Puhan, Aparna Rao, Debananda Padhy, Ganapati Panda
Retinal nerve fiber layer defect (RNFLD) provides an early objective evidence of structural changes in glaucoma. RNFLD detection is currently carried out using imaging modalities like OCT and GDx which are expensive for routine practice. In this regard, we propose a novel automatic method for RNFLD detection and angular width quantification using cost effective redfree fundus images to be practically useful for computer-assisted glaucoma risk assessment. After blood vessel inpainting and CLAHE based contrast enhancement, the initial boundary pixels are identified by local minima analysis of the 1-D intensity profiles on concentric circles...
June 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29524784/psoriasis-image-representation-using-patch-based-dictionary-learning-for-erythema-severity-scoring
#19
Yasmeen George, Mohammad Aldeen, Rahil Garnavi
Psoriasis is a chronic skin disease which can be life-threatening. Accurate severity scoring helps dermatologists to decide on the treatment. In this paper, we present a semi-supervised computer-aided system for automatic erythema severity scoring in psoriasis images. Firstly, the unsupervised stage includes a novel image representation method. We construct a dictionary, which is then used in the sparse representation for local feature extraction. To acquire the final image representation vector, an aggregation method is exploited over the local features...
June 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29523002/segmentation-of-white-matter-hyperintensities-using-convolutional-neural-networks-with-global-spatial-information-in-routine-clinical-brain-mri-with-none-or-mild-vascular-pathology
#20
Muhammad Febrian Rachmadi, Maria Del C Valdés-Hernández, Maria Leonora Fatimah Agan, Carol Di Perri, Taku Komura
We propose an adaptation of a convolutional neural network (CNN) scheme proposed for segmenting brain lesions with considerable mass-effect, to segment white matter hyperintensities (WMH) characteristic of brains with none or mild vascular pathology in routine clinical brain magnetic resonance images (MRI). This is a rather difficult segmentation problem because of the small area (i.e., volume) of the WMH and their similarity to non-pathological brain tissue. We investigate the effectiveness of the 2D CNN scheme by comparing its performance against those obtained from another deep learning approach: Deep Boltzmann Machine (DBM), two conventional machine learning approaches: Support Vector Machine (SVM) and Random Forest (RF), and a public toolbox: Lesion Segmentation Tool (LST), all reported to be useful for segmenting WMH in MRI...
June 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
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