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Retinal Image Registration under the Assumption of a Spherical Eye
Functional and structural assessment of small vessels in vivo can promote accurate diagnosis and monitor progression of diseases with a strong vasculopathy, such as hypertension and diabetes (Grosso et al., 2005). Of all the organs within the human body, the eye, particularly the retina, provides an easily accessible way to non-invasively estimate the microvascular status via fundoscopy (Abramoff et al., 2010). The analysis of the retinal structures and particularly the microvascular network is important for the diagnosis of illnesses that affect the eyesight, such as macular edema, age-related macular degeneration or gl...
Source: Computerized Medical Imaging and Graphics - June 21, 2016 Category: Radiology Authors: Carlos Hernandez-Matas, Xenophon Zabulis, Areti Triantafyllou, Panagiota Anyfanti, Antonis A. Argyros Source Type: research

WITHDRAWN: Benchmarking swarm intelligence clustering algorithms with case study of medical data
This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause.The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy. (Source: Computerized Medical Imaging and Graphics)
Source: Computerized Medical Imaging and Graphics - June 20, 2016 Category: Radiology Authors: Xiaoping Min, Liansheng Liu, Yuying He, Xueyuan Gong, Simon Fong, Qiwen Xu, Kelvin K.L. Wong Source Type: research

MRI Reconstruction with Joint Global Regularization and Transform Learning
Reconstruction for Magnetic Resonance Imaging (MRI) is an important inverse problem in biomedical image processing. Successful reconstruction of the magnetic resonance (MR) image from heavily undersampled k-space Fourier samples necessitates the employment of advanced regularization techniques. In the last decade, a primary resource for regularization has been the quest for sparsity in a transform domain. As an example, discrete total variation (TV) minimization (Chambolle, 2004) and wavelet transform sparsity based regularization (Figueiredo and Nowak, 2003) have been widely utilized in various ill-conditioned image resto...
Source: Computerized Medical Imaging and Graphics - June 20, 2016 Category: Radiology Authors: A. Korhan Tanc, Ender M. Eksioglu Source Type: research

Benchmarking Swarm Intelligence Clustering Algorithms with Case study of Medical Data
Clustering is a well known problem in computer science. Recently, some researchers applied swarm intelligence algorithms to solve the clustering problem, e.g. PSO clustering, Firefly clustering, Bat clustering, etc. Swarm intelligence algorithms are popular in the optimization community. The core idea of swarm intelligence algorithms is imitating behaviors of creatures in nature, especially those creatures that have a habit of swarming together, e.g. ants, fireflies, bees, etc. It is believed that there are some underlying reasons for their behavior, e.g. (Source: Computerized Medical Imaging and Graphics)
Source: Computerized Medical Imaging and Graphics - June 20, 2016 Category: Radiology Authors: Xiaoping Min, Liansheng Liu, Yuying He, Xueyuan Gong, Simon Fong, Qiwen Xu, Kelvin K.L. Wong Source Type: research

Watertight Modeling and Segmentation of Bifurcated Coronary Arteries for Blood Flow Simulation using CT Imaging
Image-based simulation of blood flow using computational fluid dynamics has been shown to play an important role in the diagnosis of ischemic coronary artery disease. Accurate extraction of complex coronary artery structures in a watertight geometry is a prerequisite, but manual segmentation is both tedious and subjective. Several semi- and fully automated coronary artery extraction approaches have been developed but have faced several challenges. Conventional voxel-based methods (e.g. level set) allow for watertight segmentation but are slow and difficult to incorporate expert knowledge. (Source: Computerized Medical Imaging and Graphics)
Source: Computerized Medical Imaging and Graphics - June 7, 2016 Category: Radiology Authors: Haoyin Zhou, Peng Sun, Seongmin Ha, Devon Lundine, James K. Min, Guanglei Xiong Source Type: research

A Multi-scale Tensor Voting Approach for Small Retinal Vessel Segmentation in High Resolution Fundus Images
Diabetic retinopathy (DR) is a public health issue with major implications for the eyesight of the affected population. In diabetes, blood glucose levels are higher than in the normal case. Prolonged hyperglycemia has a deteriorating effect upon retinal circulation that triggers many pathophysiological changes, of which the most important are related to the smallest vessels (Archer, 1999). If it is left untreated, this disease can progressively lead to blindness. The prevalence of blindness caused by diabetic retinopathy is 4.8% out of the total of 37 million cases throughout the world (World Health Organisation, 2005). (S...
Source: Computerized Medical Imaging and Graphics - June 5, 2016 Category: Radiology Authors: Argyrios Christodoulidis, Thomas Hurtut, Houssem Ben Tahar, Farida Cheriet Source Type: research

Retinal vessel segmentation in colour fundus images using Extreme Learning Machine
The retinal vessels are the only part of the blood circulation system of human that can be non-invasive observed directly (Resnikoff et al., 2004; Mehriban and Chen, 2012). Many systemic conditions such as diabetes, hypertension, arteriosclerosis, and cardiovascular disease can be found by the retinal vasculature examination. Therefore, the attributes of the retinal vessel such as width, tortuosity, branching pattern and angles play significant role in early disease diagnosis. Furthermore, various pathological changes of retinal vessels are direct reflections of fundus disease, such as diabetic retinopathy (DR), glaucoma, ...
Source: Computerized Medical Imaging and Graphics - May 30, 2016 Category: Radiology Authors: Chengzhang Zhu, Beiji Zou, Rongchang Zhao, Jinkai Cui, Xuanchu Duan, Zailiang Chen, Yixiong Liang Source Type: research

A segmentation and classification scheme for single tooth in MicroCT images based on 3D level set and k-means++
The tooth is one of the most important structures in the human mouth. There are a lot of diseases with the tooth, and vertical root fracture (VRF) is a severe disease in human tooth. VRF is defined as a longitudinal fracture confined to the root that usually begins on the internal canal wall and extends outward to the root surface (Yang et al., 1995). VRF is a common complication in root canal-treated teeth (Karygianni et al., 2014; Tamse, 2008). This leads to major damage to the periodontium. There exists substantial clinical evidence that VRF also generates a vertical destructive lesion (Lustig et al., 2000; Tamse et al....
Source: Computerized Medical Imaging and Graphics - May 30, 2016 Category: Radiology Authors: Liansheng Wang, Shusheng Li, Rongzhen Chen, Sze-Yu Liu, Jyh-Cheng Chen Source Type: research

A Segmentation and Classification Scheme for Single Tooth in MicroCT images based on 3D Level Set and K-means++
The tooth is one of the most important structures in the human mouth. There are a lot of diseases with the tooth, and vertical root fracture (VRF) is a severe disease in human tooth. VRF is defined as a longitudinal fracture confined to the root that usually begins on the internal canal wall and extends outward to the root surface (Yang et al., 1995). VRF is a common complication in root canal-treated teeth (Karygianni et al., 2014; Tamse, 2008). This leads to major damage to the periodontium. There exists substantial clinical evidence that VRF also generates a vertical destructive lesion (Lustig et al., 2000; Tamse et ...
Source: Computerized Medical Imaging and Graphics - May 30, 2016 Category: Radiology Authors: Liansheng Wang, Shusheng Li, Rongzhen Chen, Sze-Yu Liu, Jyh-Cheng Chen Source Type: research

Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology
Digital pathology (DP) is the process by which histology slides are digitized to produce high resolution images via whole slide digital scanners (Gurcan et al., 2009). These digitized slides afford the possibility of applying image analysis techniques to tissue images for the purpose of object detection, segmentation, and tissue classification. These automated image analysis algorithms are very relevant for nuclei detection, mitosis quantification, and tubule counting. Additionally these image analysis algorithms provide the ability of performing higher level, supervised learning tasks such as disease grading, thereby enab...
Source: Computerized Medical Imaging and Graphics - May 15, 2016 Category: Radiology Authors: Andrew Janowczyk, Ajay Basavanhally, Anant Madabhushi Source Type: research

Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology
Digital pathology (DP) is the process by which histology slides are digitized to produce high resolution images via whole slide digital scanners [1]. These digitized slides afford the possibility of applying image analysis techniques to tissue images for the purpose of object detection, segmentation, and tissue classification. These automated image analysis algorithms are very relevant for nuclei detection, mitosis quantification, and tubule counting. Additionally these image analysis algorithms provide the ability of performing higher level, supervised learning tasks such as disease grading, thereby enabling the developme...
Source: Computerized Medical Imaging and Graphics - May 15, 2016 Category: Radiology Authors: Andrew Janowczyk, Ajay Basavanhally, Anant Madabhushi Source Type: research