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 radiological and imaging diagnoses. Included in the journal will be medical scanning techniques, statistical evaluations, reports of new advances in imaging modalities, and all other information related to the medical application of computerized radiology. In addition, information on non-radiological modes of imaging that have medical applications such as ultrasound, confocal microscopy, infra-red radiation, atomic force microscopy, and other imaging modalities would be welcomed. The journal is a vehicle for the rapid publication of original research papers, review articles, and preliminary publications in the field of computerized medical imaging and graphics. Papers published in the journal should be of interest to radiologists, neurologists, neurosurgeons, ophthalmologists, proctologists, nephrologists, oncologists, and specialists in internal medicine. Please note this Journal does not accept Case Reports. http://www.medicalimagingandgraphics.com/?rss=yesElsevier Inc.en © 2010 Published by Elsevier Inc. All rights reserved. Computerized Medical Imaging and Graphics0895-6111343April 2010 © 2010 Published by Elsevier Inc. All rights reserved. healthpermissions@elsevier.comhttp://www.medicalimagingandgraphics.com/article/PIIS0895611110000170/abstract?rss=yesEditorial Board10.1016/S0895-6111(10)00017-0Computerized Medical Imaging and Graphics 34, 3 (2010)2010-04-01Computerized Medical Imaging and Graphics2010-04-01343S0895-6111(10)X0003-9CO2CO2http://www.medicalimagingandgraphics.com/article/PIIS0895611109001116/abstract?rss=yesAbstract: The neuroanatomical morphology of the optic nerve is an important description for understanding different aspects like topological distribution of nerves. Manual identification and morphometry has been usually considered as tedious, time consuming, and susceptible to error. A method that automates the identification and analysis of axons from electron micrographic images is presented. First, using region growing approach binarizes the image by combining the feature information together with spatial information, and obtains a coarse classification between myelin and non-myelin pixels. Next, identifies the axon candidates by region labeling and remove false axons on the basis of the identification ruler. Then the connected myelin sheaths are separated from each other using the maximum gradient magnitude of the outer annulus. Finally, analyses the morphological data of fibers. The developed method has been tested on a number of optic nerve images and results were presented. Regional distributions of axon caliber were unimodal. The thickness of the myelin sheath was highly correlated with the fiber diameter; hence, myelin sheath width was also distributed in a unimodal manner.Automatic identification and morphometry of optic nerve fibers in electron microscopy imagesXimei Zhao, Zhenkuan Pan, Jinyan Wu, Guomin Zhou, Yanjun Zeng10.1016/j.compmedimag.2009.08.009Computerized Medical Imaging and Graphics 34, 3 (2010)2010-04-01Computerized Medical Imaging and Graphics2010-04-01343S0895-6111(10)X0003-9Articles179184http://www.medicalimagingandgraphics.com/article/PIIS089561110900113X/abstract?rss=yesAbstract: An accurate and standardized technique for breast tumor segmentation is a critical step for monitoring and quantifying breast cancer. The fully automated tumor segmentation in mammograms presents many challenges related to characteristics of an image. In this paper, a comparison of two different semi-automated methods, viz., level set and marker controlled watershed methods that perform an accurate and fast segmentation of tumor is made. The robustness of the proposed methods is demonstrated by the segmentation of a set of 17 mammogram images. Numerical validation of the results is also provided.A comparison of two methods for the segmentation of masses in the digital mammogramsR.B. Dubey, M. Hanmandlu, S.K. Gupta10.1016/j.compmedimag.2009.09.002Computerized Medical Imaging and Graphics 34, 3 (2010)2010-04-01Computerized Medical Imaging and Graphics2010-04-01343S0895-6111(10)X0003-9Articles185191http://www.medicalimagingandgraphics.com/article/PIIS0895611109001141/abstract?rss=yesAbstract: Classifying brain white matter fibers into bundles is of growing interest in neuroscience. Quantification of diffusion characteristics inside a fiber bundle provides new insights for disease evolutions, therapy effects, and surgical interventions. In this paper, we present a novel method for segmenting fiber bundles using spherical harmonic coefficients (SHC) that describe diffusion signal obtained from High Angular Resolution Diffusion Imaging (HARDI) protocols. Based on SHC, we define a similarity measure and use it as a speed function term in level set framework. We show advantages of the proposed measure over similarity measures based on Diffusion Tensor Imaging (DTI) indices. Without any assumptions about diffusion model, we deal with diffusion signal instead of orientation distribution function (ODF) calculated using complicated mathematics, inaccurate simplifications, and time-consuming implementation. By applying the proposed algorithm on synthetic data, its superior accuracy and robustness in low SNR conditions are shown. Application of the proposed method on real HARDI MRI data also illustrates its superior performance, especially in heterogeneous diffusion areas with low traditional diffusion anisotropies.Level set fiber bundle segmentation using spherical harmonic coefficientsMohammad-Reza Nazem-Zadeh, Esmaeil Davoodi-Bojd, Hamid Soltanian-Zadeh10.1016/j.compmedimag.2009.09.003Computerized Medical Imaging and Graphics 34, 3 (2010)2010-04-01Computerized Medical Imaging and Graphics2010-04-01343S0895-6111(10)X0003-9Articles192202http://www.medicalimagingandgraphics.com/article/PIIS0895611109001153/abstract?rss=yesAbstract: Retinal blood vessels are important objects in ophthalmologic images. In spite of many attempts for vessel detection, it appears that existing methodologies are based on edge detection or modeling of vessel cross-sectional profiles in intensity. The application of these methodologies is hampered by the presence of a wide range of retinal vessels. In this paper we define a universal representation for upward and downward vessel cross-sectional profiles with varying boundary sharpness. This expression is used to define a new scheme of vessel detection based on symmetry and asymmetry in the Fourier domain. Phase congruency is utilized for measuring symmetry and asymmetry so that our scheme is invariant to vessel brightness variations. We have performed experiments on fluorescein images and color fundus images to show the efficiency of the proposed algorithm technique. We also have performed a width measurement study, using an optimal medial axis skeletonization scheme as a post-processing step, to compare the technique with the generalized Gaussian profile modeling. The new algorithm technique is promising for automated vessel detection where optimizing profile models is difficult and preserving vessel width information is necessary.Fourier cross-sectional profile for vessel detection on retinal imagesTao Zhu10.1016/j.compmedimag.2009.09.004Computerized Medical Imaging and Graphics 34, 3 (2010)2010-04-01Computerized Medical Imaging and Graphics2010-04-01343S0895-6111(10)X0003-9Articles203212http://www.medicalimagingandgraphics.com/article/PIIS0895611109001177/abstract?rss=yesAbstract: In this paper an algorithm for vessel segmentation and network extraction in retinal images is proposed. A new multi-scale line-tracking procedure is starting from a small group of pixels, derived from a brightness selection rule, and terminates when a cross-sectional profile condition becomes invalid. The multi-scale image map is derived after combining the individual image maps along scales, containing the pixels confidence to belong in a vessel. The initial vessel network is derived after map quantization of the multi-scale confidence matrix. Median filtering is applied in the initial vessel network, restoring disconnected vessel lines and eliminating noisy lines. Finally, post-processing removes erroneous areas using directional attributes of vessels and morphological reconstruction.The experimental evaluation in the publicly available DRIVE database shows accurate extraction of vessels network. The average accuracy of 0.929 with 0.747 sensitivity and 0.955 specificity is very close to the manual segmentation rates obtained by the second observer. The proposed algorithm is compared also with widely used supervised and unsupervised methods and evaluated in noisy conditions, giving higher average sensitivity rate in the same range of specificity and accuracy, and showing robustness in the presence of additive Salt&Pepper or Gaussian white noise.Multi-scale retinal vessel segmentation using line trackingMarios Vlachos, Evangelos Dermatas10.1016/j.compmedimag.2009.09.006Computerized Medical Imaging and Graphics 34, 3 (2010)2010-04-01Computerized Medical Imaging and Graphics2010-04-01343S0895-6111(10)X0003-9Articles213227http://www.medicalimagingandgraphics.com/article/PIIS0895611109001293/abstract?rss=yesAbstract: The detection of exudates is a prerequisite for detecting and grading severe retinal lesions, like the diabetic macular edema. In this work, we present a new method based on mathematical morphology for detecting exudates in color eye fundus images. A preliminary evaluation of the proposed method performance on a known public database, namely DIARETDB1, indicates that it can achieve an average sensitivity of 70.48%, and an average specificity of 98.84%. Comparing to other recent automatic methods available in the literature, our proposed approach potentially can obtain better exudate detection results in terms of sensitivity and specificity.A coarse-to-fine strategy for automatically detecting exudates in color eye fundus imagesDaniel Welfer, Jacob Scharcanski, Diane Ruschel Marinho10.1016/j.compmedimag.2009.10.001Computerized Medical Imaging and Graphics 34, 3 (2010)2010-04-01Computerized Medical Imaging and Graphics2010-04-01343S0895-6111(10)X0003-9Articles228235http://www.medicalimagingandgraphics.com/article/PIIS089561110900130X/abstract?rss=yesAbstract: This paper presents an automatic method for segmentation of brain structures using their symmetry and tissue type information. The proposed method generates segmented structures that have homogenous tissues. It benefits from general symmetry of the brain structures in the two hemispheres. It also benefits from the tissue regions generated by fuzzy c-means clustering. All in all, the proposed method can be described as a dynamic knowledge-based method that eliminates the need for statistical shape models of the structures while generating accurate segmentation results. The proposed approach is implemented in MATLAB and tested on the Internet Brain Segmentation Repository (IBSR) datasets. To this end, it is applied to the segmentation of caudate and ventricles three-dimensionally in magnetic resonance images (MRI) of the brain. Impacts of each of the steps of the proposed approach are demonstrated through experiments. It is shown that the proposed method generates accurate segmentation results that are insensitive to initialization and parameter selection. The proposed method is compared to four previous methods illustrating advantages and limitations of each method.Three-dimensional coupled-object segmentation using symmetry and tissue type informationPayam B. Bijari, Alireza Akhondi-Asl, Hamid Soltanian-Zadeh10.1016/j.compmedimag.2009.10.002Computerized Medical Imaging and Graphics 34, 3 (2010)2010-04-01Computerized Medical Imaging and Graphics2010-04-01343S0895-6111(10)X0003-9Articles236249