Computerized Medical Imaging and Graphics RSS feed: Current Issue. The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of information concerning the medical use of new developments in imaging diagnosis, intervention, and follow up. Included in the journal will be articles on new medical scanning techniques, image-guided therapy, computer-aided diagnosis, robotic surgery and imaging, augmented-reality medical visualization, imaging genomics, reports of new advances in imaging modalities, and all other information related to the application of computerized radiology, oncology, and surgery. In addition, information on non-medical modes of imaging that have medical applications such as confocal and multi-photon microscopy, optical microendoscope, photoacoustic imaging, infra-red radiation, and other imaging modalities would be welcomed. The journal is a vehicle for the rapid publication of original research papers and review articles in the field of computerized medical imaging and graphics. Papers published in the journal will be of interest to computer scientists, medical physicists, bioengineers, imaging specialists, medical informaticians, computational biologists, radiologists, oncologists, neurologists, neurosurgeons, surgeons, cardiologists, cardiovascular surgeons, nuclear medicine physicians, pathologists, ophthalmologists, proctologists, nephrologists, gastroenterologists, and internists interested in imaging applications. Please note this Journal accepts neither Case Reports nor preliminary publications. http://www.medicalimagingandgraphics.com/?rss=yesElsevier Inc.en © 2012 Published by Elsevier Inc. All rights reserved. Computerized Medical Imaging and Graphics0895-6111362March 2012 © 2012 Published by Elsevier Inc. All rights reserved. healthpermissions@elsevier.comhttp://www.medicalimagingandgraphics.com/article/PIIS0895611112000055/abstract?rss=yesEditorial Board10.1016/S0895-6111(12)00005-5Computerized Medical Imaging and Graphics 36, 2 (2012)2012-03-01Computerized Medical Imaging and Graphics2012-03-01362S0895-6111(12)X0002-8CO2CO2http://www.medicalimagingandgraphics.com/article/PIIS0895611111001042/abstract?rss=yesAbstract: One major source affecting the precision of bone structure analysis in quantitative magnetic resonance imaging (qMRI) is inter- and intraoperator variability, inherent in delineating and tracing regions of interest along longitudinal studies. In this paper an automated analysis tool, featuring bone marrow segmentation, region of interest generation, and characterization of cancellous bone of articular joints is presented. In evaluation studies conducted at the knee joint the novel analysis tool significantly decreased the standard error of measurement and improved the sensitivity in detecting minor structural changes. It further eliminated the need of time-consuming user interaction, and thereby increasing reproducibility.A fully automated trabecular bone structural analysis tool based on T2*-weighted magnetic resonance imagingMarkus Kraiger, Petros Martirosian, Peter Opriessnig, Frank Eibofner, Hansjoerg Rempp, Michael Hofer, Fritz Schick, Rudolf Stollberger10.1016/j.compmedimag.2011.07.006Computerized Medical Imaging and Graphics 36, 2 (2012)2011-08-22Computerized Medical Imaging and Graphics2011-08-22362S0895-6111(12)X0002-8MRI, bone marrow8594http://www.medicalimagingandgraphics.com/article/PIIS0895611111000796/abstract?rss=yesAbstract: A significant medical informatics task is indexing patient databases according to size, location, and other characteristics of brain tumors and edemas, possibly based on magnetic resonance (MR) imagery. This requires segmenting tumors and edemas within images from different MR modalities. To date, automated brain tumor or edema segmentation from MR modalities remains a challenging, computationally intensive task. In this paper, we propose a novel automated, fast, and approximate segmentation technique. The input is a patient study consisting of a set of MR slices, and its output is a subset of the slices that include axis-parallel boxes that circumscribe the tumors. Our approach is based on an unsupervised change detection method that searches for the most dissimilar region (axis-parallel bounding boxes) between the left and the right halves of a brain in an axial view MR slice. This change detection process uses a novel score function based on Bhattacharya coefficient computed with gray level intensity histograms. We prove that this score function admits a very fast (linear in image height and width) search to locate the bounding box. The average dice coefficients for localizing brain tumors and edemas, over ten patient studies, are 0.57 and 0.52, respectively, which significantly exceeds the scores for two other competitive region-based bounding box techniques.Quick detection of brain tumors and edemas: A bounding box method using symmetryBaidya Nath Saha, Nilanjan Ray, Russell Greiner, Albert Murtha, Hong Zhang10.1016/j.compmedimag.2011.06.001Computerized Medical Imaging and Graphics 36, 2 (2012)2011-07-04Computerized Medical Imaging and Graphics2011-07-04362S0895-6111(12)X0002-8MRI, brain tumor95107http://www.medicalimagingandgraphics.com/article/PIIS0895611111000838/abstract?rss=yesAbstract: Renal diseases can lead to kidney failure that requires life-long dialysis or renal transplantation. Early detection and treatment can prevent progression towards end stage renal disease. MRI has evolved into a standard examination for the assessment of the renal morphology and function. We propose a wavelet-based clustering to group the voxel time courses and thereby, to segment the renal compartments. This approach comprises (1) a nonparametric, discrete wavelet transform of the voxel time course, (2) thresholding of the wavelet coefficients using Stein's Unbiased Risk estimator, and (3) k-means clustering of the wavelet coefficients to segment the kidneys. Our method was applied to 3D dynamic contrast enhanced (DCE-) MRI data sets of human kidney in four healthy volunteers and three patients. On average, the renal cortex in the healthy volunteers could be segmented at 88%, the medulla at 91%, and the pelvis at 98% accuracy. In the patient data, with aberrant voxel time courses, the segmentation was also feasible with good results for the kidney compartments. In conclusion wavelet based clustering of DCE-MRI of kidney is feasible and a valuable tool towards automated perfusion and glomerular filtration rate quantification.Wavelet-based segmentation of renal compartments in DCE-MRI of human kidney: Initial results in patients and healthy volunteersSheng Li, Frank G. Zöllner, Andreas D. Merrem, Yinghong Peng, Jarle Roervik, Arvid Lundervold, Lothar R. Schad10.1016/j.compmedimag.2011.06.005Computerized Medical Imaging and Graphics 36, 2 (2012)2011-06-27Computerized Medical Imaging and Graphics2011-06-27362S0895-6111(12)X0002-8DCE-MRI, kidney108118http://www.medicalimagingandgraphics.com/article/PIIS0895611111000991/abstract?rss=yesAbstract: This study directly compares morphological features of the mouse heart in its end-relaxed state based on constructed morphometric maps and atlases using principal component analysis in C57BL/6J (n=8) and DBA (n=5) mice. In probabilistic atlases, a gradient probability exists for both strains in longitudinal locations from base to apex. Based on the statistical atlases, differences in size (49.8%), apical direction (15.6%), basal ventricular blood pool size (13.2%), and papillary muscle shape and position (17.2%) account for the most significant modes of shape variability for the left ventricle of the C57BL/6J mice. For DBA mice, differences in left ventricular size and direction (67.4%), basal size (15.7%), and position of papillary muscles (16.8%) account for significant variability.Morphological studies of the murine heart based on probabilistic and statistical atlasesDimitrios Perperidis, Elizabeth Bucholz, G. Allan Johnson, Christakis Constantinides10.1016/j.compmedimag.2011.07.001Computerized Medical Imaging and Graphics 36, 2 (2012)2011-08-08Computerized Medical Imaging and Graphics2011-08-08362S0895-6111(12)X0002-8MRI,murine heart119129http://www.medicalimagingandgraphics.com/article/PIIS0895611111001194/abstract?rss=yesAbstract: Due to physical limitations inherent in magnetic resonance imaging scanners, three dimensional volumetric scans are often acquired with anisotropic voxel resolution. We investigate several interpolation approaches to reduce the anisotropy and present a novel approach – constrained reverse diffusion for thick slice interpolation. This technique was compared to common methods: linear and cubic B-Spline interpolation and a technique based on non-rigid registration of neighboring slices. The methods were evaluated on artificial MR phantoms and real MR scans of human brain. The constrained reverse diffusion approach delivered promising results and provides an alternative for thick slice interpolation, especially for higher anisotropy factors.Constrained reverse diffusion for thick slice interpolation of 3D volumetric MRI imagesAleš Neubert, Olivier Salvado, Oscar Acosta, Pierrick Bourgeat, Jurgen Fripp10.1016/j.compmedimag.2011.08.004Computerized Medical Imaging and Graphics 36, 2 (2012)2011-09-19Computerized Medical Imaging and Graphics2011-09-19362S0895-6111(12)X0002-8MRI, brain130138http://www.medicalimagingandgraphics.com/article/PIIS0895611111001200/abstract?rss=yesAbstract: In this paper, a novel image registration method is proposed to achieve accurate registration between images having large shape differences with the help of a set of appropriate intermediate templates. We first demonstrate that directionality is a key factor in both pairwise image registration and groupwise registration, which is defined in this paper to describe the influence of the registration direction and paths on the registration performance. In our solution, the intermediate template selection and intermediate template guided registration are two coherent steps with directionality being considered. To take advantage of the directionality, a directed graph is built based on the asymmetric distance defined on all ordered image pairs in the image population, which is fundamentally different from the undirected graph with symmetric distance metrics in all previous methods, and the shortest distance between template and subject on the directed graph is calculated. The allocated directed path can be thus utilized to better guide the registration by successively registering the subject through the intermediate templates one by one on the path towards the template. The proposed directed graph based solution can also be used in groupwise registration. Specifically, by building a minimum spanning arborescence (MSA) on the directed graph, the population center, i.e., a selected template, as well as the directed registration paths from all the rest of images to the population center, is determined simultaneously. The performance of directed graph based registration algorithm is demonstrated by the spatial normalization on both synthetic dataset and real brain MR images. It is shown that our method can achieve more accurate registration results than both the undirected graph based solution and the direct pairwise registration.Directed graph based image registrationHongjun Jia, Guorong Wu, Qian Wang, Yaping Wang, Minjeong Kim, Dinggang Shen10.1016/j.compmedimag.2011.09.001Computerized Medical Imaging and Graphics 36, 2 (2012)2011-10-20Computerized Medical Imaging and Graphics2011-10-20362S0895-6111(12)X0002-8MRI, brain139151http://www.medicalimagingandgraphics.com/article/PIIS0895611111001224/abstract?rss=yesAbstract: In this paper, we propose a framework to automate the assessment of the movements of a third cerebral ventricle in a cine MR sequence. Indeed, the goal of this assessment is to build an atlas of the movements of the healthy ventricles in the context of the hydrocephalus pathology. This approach is composed of two phases: a contour extraction, using fractional integration and a registration method, based on dynamic evolutionary optimization. The first phase of the framework is based on the fractional integration thresholding, that allows delineating the contours of the area of interest. In order to track over time each point of the primitive and achieve the assessment of the deformation, a matching method, based on a new dynamic optimization algorithm, called Dynamic Covariance Matrix Adaptation Evolution Strategy (D-CMAES), is used. The obtained results for quantification have been clinically validated by an expert and compared to those presented in the literature.A framework for analysis of brain cine MR sequencesAmir Nakib, Patrick Siarry, Philippe Decq10.1016/j.compmedimag.2011.09.003Computerized Medical Imaging and Graphics 36, 2 (2012)2011-10-28Computerized Medical Imaging and Graphics2011-10-28362S0895-6111(12)X0002-8MRI, cerebral ventricle152168http://www.medicalimagingandgraphics.com/article/PIIS0895611111001509/abstract?rss=yesThe authors regret that incorrectly numbered references appeared in Table 4 of this article. The authors would like to apologise for any inconvenience caused. A corrected Table 4 appears below.Corrigendum to “Computer-assisted detection of infectious lung diseases: A review” [Comput. Med. Imag. Graph. 36 (2012) 72–84]Ulaş Bağcı, Mike Bray, Jesus Caban, Jianhua Yao, Daniel J. Mollura10.1016/j.compmedimag.2011.12.003Computerized Medical Imaging and Graphics 36, 2 (2012)2012-01-19Computerized Medical Imaging and Graphics2012-01-19362S0895-6111(12)X0002-8Corrigendum169169http://www.medicalimagingandgraphics.com/article/PIIS0895611112000134/abstract?rss=yesSpecial Issue Call for Papers10.1016/S0895-6111(12)00013-4Computerized Medical Imaging and Graphics 36, 2 (2012)2012-03-01Computerized Medical Imaging and Graphics2012-03-01362S0895-6111(12)X0002-8II