Computerized Medical Imaging and Graphics RSS feed: Articles in Press. 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 radiologists, oncologists, neurologists, neurosurgeons, surgeons, nuclear medicine physicians, ophthalmologists, proctologists, nephrologists, gastroenterologists, bioengineers, medical physicists, and imaging specialists in internal medicine. Please note this Journal accepts neither Case Reports nor preliminary publications. http://www.medicalimagingandgraphics.com//inpress?rss=yesElsevier Inc.en © 2010 Elsevier Ltd. All rights reserved. Computerized Medical Imaging and Graphics0895-61112010-09-01 © 2010 Elsevier Ltd. All rights reserved. healthpermissions@elsevier.comhttp://www.medicalimagingandgraphics.com/article/PIIS0895611110000789/abstract?rss=yesAbstract: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Automated assessment tools for dermoscopy images have become an important research field mainly because of inter- and intra-observer variations in human interpretation. One of the most important steps in dermoscopy image analysis is automated detection of lesion borders.In this study, we introduce a border-driven density-based framework to identify skin lesion(s) in dermoscopy images. Unlike the conventional density-based clustering algorithms, proposed algorithm expands regions only at borders of a cluster that in turn speeds up the process without losing precision or recall. In our method, border regions are represented with one or more simple polygons at any time. We tested our algorithm on a dataset of 100 dermoscopy cases with multiple physicians’ drawn ground truth borders. The results show that border error and f-measure of assessment averages out at 6.9% and 0.86 respectively.Fast density-based lesion detection in dermoscopy images - Corrected ProofMutlu Mete, Sinan Kockara, Kemal Aydin10.1016/j.compmedimag.2010.07.007Computerized Medical Imaging and Graphics (2010)2010-09-01Computerized Medical Imaging and Graphics2010-09-01http://www.medicalimagingandgraphics.com/article/PIIS0895611110000765/abstract?rss=yesAbstract: Rank et al. have proposed an algorithm for estimating image noise variance composed of the following three steps: the noisy image is first filtered by a difference operator; a histogram of local signal variances is then computed; and, finally the noise variance is estimated from a statistical evaluation of the histogram. We have verified the accuracy of this algorithm on a CT image by indirect methods, and have shown that this method is able to estimate CT image noise variance with reasonable accuracy, regardless of whether or not the noiseless image is uniform. Further, we have proposed a simple alternative method for the last two steps of the Rank et al. method. However, one must pay attention to the fact that the estimated noise variance will be biased when the nearest two pixels are correlated and that this algorithm does not work well if the assumption of stationarity of noise components is violated.A method for estimating noise variance of CT image - Corrected ProofMitsuru Ikeda, Reiko Makino, Kuniharu Imai, Maiko Matsumoto, Rika Hitomi10.1016/j.compmedimag.2010.07.005Computerized Medical Imaging and Graphics (2010)2010-08-27Computerized Medical Imaging and Graphics2010-08-27http://www.medicalimagingandgraphics.com/article/PIIS0895611110000753/abstract?rss=yesAbstract: In this paper, we propose a H.264/AVC based compression technique adapted to coronary angiograms. H.264/AVC coder has proven to use the most advanced and accurate motion compensation process, but, at the cost of high computational complexity. On the other hand, analysis of coronary X-ray images reveals large areas containing no diagnostically important information. Our contribution is to exploit the energy characteristics in slice equal size regions to determine the regions with relevant information content, to be encoded using the H.264 coding paradigm. The others regions, are compressed using fixed block motion compensation and conventional hard-decision quantization. Experiments have shown that at the same bitrate, this procedure reduces the H.264 coder computing time of about 25% while attaining the same visual quality. A subjective assessment, based on the consensus approach leads to a compression ratio of 30:1 which insures both a diagnostic adequacy and a sufficient compression in regards to storage and transmission requirements.Coronary angiogram video compression for remote browsing and archiving applications - Corrected ProofAzza Ouled Zaid, Bilel Ben Fradj10.1016/j.compmedimag.2010.07.004Computerized Medical Imaging and Graphics (2010)2010-08-26Computerized Medical Imaging and Graphics2010-08-26http://www.medicalimagingandgraphics.com/article/PIIS0895611110000777/abstract?rss=yesAbstract: Efficient visualization of vascular structures is essential for therapy planning and medical education. Existing techniques achieve high-quality visualization of vascular surfaces at the cost of low rendering speed and large size of resulting surface. In this paper, we present an approach for visualizing vascular structures by exploiting the local curvature information of a given surface. To handle complex topology of loop and multiple parents and/or multiple children, bidirectional adaptive sampling and modified normal calculations at joints are proposed. The proposed method has been applied to cerebral vascular trees, liver vessel trees, and aortic vessel trees. The experimental results show that it can obtain a high-quality surface visualization with fewer polygons in the approximation.Curvature-dependent surface visualization of vascular structures - Corrected ProofJianhuang Wu, Renhui Ma, Xin Ma, Fucang Jia, Qingmao Hu10.1016/j.compmedimag.2010.07.006Computerized Medical Imaging and Graphics (2010)2010-08-23Computerized Medical Imaging and Graphics2010-08-23http://www.medicalimagingandgraphics.com/article/PIIS0895611110000674/abstract?rss=yesAbstract: We describe a novel approach to detect and visualize pigment network structures in dermoscopic images, based on the fact that the edges of pigment network structures form cyclic graphs which can be automatically detected and analyzed. First we perform a pre-processing step of image enhancement and edge detection. The resulting binary edge image is converted to a graph and the defined feature patterns are extracted by finding cyclic subgraphs corresponding to skin texture structures. We filtered these cyclic subgraphs to remove other round structures such as globules, dots, and oil bubbles, based on their size and color. Another high-level graph is created from each correctly extracted subgraph, with a node corresponding to a hole in the pigment network. Nodes are connected by edges according to their distances. Finally the image is classified according to the density ratio of the graph. Our results over a set of 500 images from a well known atlas of dermoscopy show an accuracy of 94.3% on classification of the images as pigment network Present or Absent.A novel method for detection of pigment network in dermoscopic images using graphs - Corrected ProofMaryam Sadeghi, Majid Razmara, Tim K. Lee, M.Stella Atkins10.1016/j.compmedimag.2010.07.002Computerized Medical Imaging and Graphics (2010)2010-08-19Computerized Medical Imaging and Graphics2010-08-19http://www.medicalimagingandgraphics.com/article/PIIS0895611110000741/abstract?rss=yesAbstract: Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging.Medical image analysis with artificial neural networks - Corrected ProofJ. Jiang, P. Trundle, J. Ren10.1016/j.compmedimag.2010.07.003Computerized Medical Imaging and Graphics (2010)2010-08-16Computerized Medical Imaging and Graphics2010-08-16http://www.medicalimagingandgraphics.com/article/PIIS0895611110000662/abstract?rss=yesAbstract: The development of an integrated MultiSpectral Imaging (MSI) system yielding hyperspectral cubes by means of artificial neural networks is described. The MSI system is based on a CCD camera, a rotating wheel bearing a set of seven interference filters, a light source and a computer. The resulting device has been elaborated for in vivo imaging of skin lesions. It provides multispectral images and is coupled with a software reconstructing hyperspectral cubes from multispectral images. Reconstruction is performed by a neural network-based algorithm using heteroassociative memories. The resulting hyperspectral cube provides skin optical reflectance spectral data combined with bidimensional spatial information. This combined information will hopefully improve diagnosis and follow-up in a range of skin disorders from skin cancer to inflammatory diseases.Reconstruction of hyperspectral cutaneous data from an artificial neural network-based multispectral imaging system - Corrected ProofRomuald Jolivot, Pierre Vabres, Franck Marzani10.1016/j.compmedimag.2010.07.001Computerized Medical Imaging and Graphics (2010)2010-08-09Computerized Medical Imaging and Graphics2010-08-09http://www.medicalimagingandgraphics.com/article/PIIS0895611110000534/abstract?rss=yesAbstract: Automated segmentation and analysis of tree-like structures from 3D medical images are important for many medical applications, such as those dealing with blood vasculature or lung airways. However, there is an absence of large databases of expert segmentations and analyses of such 3D medical images, which impedes the validation and training of proposed image analysis algorithms. In this work, we simulate volumetric images of vascular trees and generate the corresponding ground-truth segmentations, bifurcation locations, branch properties, and tree hierarchy. The tree generation is performed by iteratively growing a vascular structure based on a user-defined (possibly spatially varying) oxygen demand map. We describe the details of the algorithm and provide a variety of example results.VascuSynth: Simulating vascular trees for generating volumetric image data with ground-truth segmentation and tree analysis - Corrected ProofGhassan Hamarneh, Preet Jassi10.1016/j.compmedimag.2010.06.002Computerized Medical Imaging and Graphics (2010)2010-07-26Computerized Medical Imaging and Graphics2010-07-26http://www.medicalimagingandgraphics.com/article/PIIS0895611110000510/abstract?rss=yesAbstract: Segmentation is a fundamental component of many medical image-processing applications, and it has long been recognized as a challenging problem. In this paper, we report our research and development efforts on analyzing and extracting clinically meaningful regions from uterine cervix images in a large database created for the study of cervical cancer. In addition to proposing new algorithms, we also focus on developing open source tools which are in synchrony with the research objectives. These efforts have resulted in three Web-accessible tools which address three important and interrelated sub-topics in medical image segmentation, respectively: the Boundary Marking Tool (BMT), Cervigram Segmentation Tool (CST), and Multi-Observer Segmentation Evaluation System (MOSES). The BMT is for manual segmentation, typically to collect “ground truth” image regions from medical experts. The CST is for automatic segmentation, and MOSES is for segmentation evaluation. These tools are designed to be a unified set in which data can be conveniently exchanged. They have value not only for improving the reliability and accuracy of algorithms of uterine cervix image segmentation, but also promoting collaboration between biomedical experts and engineers which are crucial to medical image-processing applications. Although the CST is designed for the unique characteristics of cervigrams, the BMT and MOSES are very general and extensible, and can be easily adapted to other biomedical image collections.A unified set of analysis tools for uterine cervix image segmentation - Corrected ProofZhiyun Xue, L. Rodney Long, Sameer Antani, Leif Neve, Yaoyao Zhu, George R. Thoma10.1016/j.compmedimag.2010.04.002Computerized Medical Imaging and Graphics (2010)2010-06-01Computerized Medical Imaging and Graphics2010-06-01