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 Graphics0895-6111342March 2010 © 2010 Published by Elsevier Inc. All rights reserved. healthpermissions@elsevier.comhttp://www.medicalimagingandgraphics.com/article/PIIS0895611110000042/abstract?rss=yesEditorial Board10.1016/S0895-6111(10)00004-2Computerized Medical Imaging and Graphics 34, 2 (2010)2010-03-01Computerized Medical Imaging and Graphics2010-03-01342S0895-6111(10)X0002-7CO2CO2http://www.medicalimagingandgraphics.com/article/PIIS0895611109000858/abstract?rss=yesAbstract: We introduce a novel approach to specify and edit volumes of interest (VOI for short) interactively. Enhancing the capabilities of standard systems we provide tools to edit the VOI by defining a not necessarily convex polyhedral bounding object. We suggest to use low-level editing interactions for moving, inserting and deleting vertices, edges and faces of the polyhedron. The low-level operations can be used as building blocks for more complex higher order operations fitting the application demands. Flexible initialization allows the user to select within a few clicks convex VOI that in the classical clipping plane model need the specification of a large number of cutting planes. In our model it is similarly simple to select non-convex VOI. Boolean combinations allow to select non-connected VOI of arbitrary complexity. The polyhedral VOI selection technique enables the user to define VOI with complex boundary structure interactively, in an easy to comprehend and predictable manner.Non-convex polyhedral volume of interest selectionRaphael Fuchs, Volkmar Welker, Joachim Hornegger10.1016/j.compmedimag.2009.07.002Computerized Medical Imaging and Graphics 34, 2 (2010)2010-03-01Computerized Medical Imaging and Graphics2010-03-01342S0895-6111(10)X0002-7Articles105113http://www.medicalimagingandgraphics.com/article/PIIS0895611109000871/abstract?rss=yesAbstract: Anatomically precise geometric models of human airways and arterial vessels play a critical role in the analysis of air and blood flows in human bodies. The established geometric modelling methods become invalid when the model consists of bronchioles or small vessels. This paper presents a new method for reconstructing the entire airway tree and carotid vessels from point clouds obtained from CT or MR images. A novel layer-by-layer searching algorithm has been developed to recognize branches of the airway tree and arterial vessels from the point clouds. Instead of applying uniform accuracy to all branches regardless of the number of available points, the surface patches on each branch are constructed adaptively based on the number of available elemental points, which leads to the elimination of distortions occurring at small bronchi and vessels.Region-based geometric modelling of human airways and arterial vesselsSonglin Ding, Yong Ye, Jiyuan Tu, Aleksandar Subic10.1016/j.compmedimag.2009.07.005Computerized Medical Imaging and Graphics 34, 2 (2010)2010-03-01Computerized Medical Imaging and Graphics2010-03-01342S0895-6111(10)X0002-7Articles114121http://www.medicalimagingandgraphics.com/article/PIIS0895611109000883/abstract?rss=yesAbstract: Pathogenic protozoan parasites can cause human to get many diseases, such as, amoebiasis, typhoid fever and cholera, etc. Different protozoan parasites vary greatly in their structural and biochemical properties. Digital images are extensively applied to medical fields for doctors and pathologists to analyze pathological sections and further diagnose diseases. The aim of this paper is to develop protozoan parasite extraction techniques to segment protozoan parasites from microscopic images. The proposed scheme has precise segmentation ability even if the image is with poor quality or complex background. Experimental results show that the proposed scheme can gain 96.64% average correct rate, and about 0.04, 0.45 and 0.06 of the average error rates: misclassification error (ME), region non-uniformity (RN) and relative foreground area error (RFAE), respectively.A protozoan parasite extraction scheme for digital microscopic imagesChing-Hao Lai, Shyr-Shen Yu, Hsiao-Yun Tseng, Meng-Hsiun Tsai10.1016/j.compmedimag.2009.07.008Computerized Medical Imaging and Graphics 34, 2 (2010)2010-03-01Computerized Medical Imaging and Graphics2010-03-01342S0895-6111(10)X0002-7Articles122130http://www.medicalimagingandgraphics.com/article/PIIS0895611109000913/abstract?rss=yesAbstract: An empirical stopping criterion for the 2D-maximum-likelihood expectation–maximization (MLEM) iterative image reconstruction algorithm in positron emission tomography (PET) has been proposed. We have applied the MLEM algorithm on Monte Carlo generated noise-free projection data and studied the properties of the pixel updating coefficients (PUC) in the reconstructed images. Appropriate fitting lead to an analytical expression for the parameterization of the minimum value in the PUC vector for all non-zero pixels for a given number of detected counts, which can be employed as basis for the stopping criterion proposed. These results have been validated with simulated data from real PET images.PET image reconstruction: A stopping rule for the MLEM algorithm based on properties of the updating coefficientsAnastasios Gaitanis, George Kontaxakis, George Spyrou, George Panayiotakis, George Tzanakos10.1016/j.compmedimag.2009.07.006Computerized Medical Imaging and Graphics 34, 2 (2010)2010-03-01Computerized Medical Imaging and Graphics2010-03-01342S0895-6111(10)X0002-7Articles131141http://www.medicalimagingandgraphics.com/article/PIIS0895611109000998/abstract?rss=yesAbstract: This paper proposes a state space method for limited view PET reconstruction. Due to the high-level of noise and data-incompletion, prior knowledge is required to guide PET recovery. The compartmental model is used as an evolution equation to regularize the dynamic reconstruction. The continuous–discrete Kalman filter is adopted to calculate the radioactivity value recursively. With tracer kinetic information as prior, the state space approach can obtain a better result compared with the MLEM algorithm. The identifiability of this method is proved by computer synthetic simulation and real phantom experiment on the Hamamatsu SHR-22000PET scanner.Limited view PET reconstruction of tissue radioactivity mapsYunxia Shen, Huafeng Liu, Pengcheng Shi10.1016/j.compmedimag.2009.07.009Computerized Medical Imaging and Graphics 34, 2 (2010)2010-03-01Computerized Medical Imaging and Graphics2010-03-01342S0895-6111(10)X0002-7Articles142148http://www.medicalimagingandgraphics.com/article/PIIS0895611109001062/abstract?rss=yesAbstract: A method for motion compensated iterative CT reconstruction of a cardiac region-of-interest is presented. The algorithm is an ordered subset maximum likelihood approach with spherically symmetric basis functions, and it uses an ECG for gating. Since the straightforward application of iterative methods to CT data has the drawback that a field-of-view has to be reconstructed, which covers the complete volume contributing to the absorption, region-of-interest reconstruction is applied here. Despite gating, residual object motion within the reconstructed gating window leads to motion blurring in the reconstructed image. To limit this effect, motion compensation is applied. Hereto, a gated 4D reconstruction at multiple phases is generated for the region-of-interest, and a limited set of vascular landmarks are manually annotated throughout the cardiac phases. A dense motion vector field is obtained from these landmarks by scattered data interpolation. The method is applied to two clinical data sets at strongest motion phases. Comparing the method to standard gated iterative reconstruction results shows that motion compensation strongly improved reconstruction quality.Motion compensated iterative reconstruction of a region of interest in cardiac cone-beam CTA.A. Isola, A. Ziegler, D. Schäfer, T. Köhler, W.J. Niessen, M. Grass10.1016/j.compmedimag.2009.08.004Computerized Medical Imaging and Graphics 34, 2 (2010)2010-03-01Computerized Medical Imaging and Graphics2010-03-01342S0895-6111(10)X0002-7Articles149159http://www.medicalimagingandgraphics.com/article/PIIS0895611109001074/abstract?rss=yesAbstract: This paper presents a statistical method for the detection of lobar pneumonia when using digitized chest X-ray films. Each region of interest was represented by a vector of wavelet texture measures which is then multiplied by the orthogonal matrix Q2. The first two elements of the transformed vectors were shown to have a bivariate normal distribution. Misclassification probabilities were estimated using probability ellipsoids and discriminant functions. The result of this study recommends the detection of pneumonia by constructing probability ellipsoids or discriminant function using maximum energy and maximum column sum energy texture measures where misclassification probabilities were less than 0.15.A discrimination method for the detection of pneumonia using chest radiographNorliza Mohd. Noor, Omar Mohd. Rijal, Ashari Yunus, S.A.R. Abu-Bakar10.1016/j.compmedimag.2009.08.005Computerized Medical Imaging and Graphics 34, 2 (2010)2010-03-01Computerized Medical Imaging and Graphics2010-03-01342S0895-6111(10)X0002-7Articles160166http://www.medicalimagingandgraphics.com/article/PIIS0895611109001104/abstract?rss=yesAbstract: A novel denoising approach is proposed that is based on a spectral data substitution mechanism through using a mathematical model of one-dimensional singularity function analysis (1-D SFA). The method consists in dividing the complete spectral domain of the noisy signal into two subsets: the preserved set where the spectral data are kept unchanged, and the substitution set where the original spectral data having lower signal-to-noise ratio (SNR) are replaced by those reconstructed using the 1-D SFA model. The preserved set containing original spectral data is determined according to the SNR of the spectrum. The singular points and singularity degrees in the 1-D SFA model are obtained through calculating finite difference of the noisy signal. The theoretical formulation and experimental results demonstrated that the proposed method allows more efficient denoising while introducing less distortion, and presents significant improvement over conventional denoising methods.Medical image denoising using one-dimensional singularity function modelJianhua Luo, Yuemin Zhu, Bassem Hiba10.1016/j.compmedimag.2009.08.007Computerized Medical Imaging and Graphics 34, 2 (2010)2010-03-01Computerized Medical Imaging and Graphics2010-03-01342S0895-6111(10)X0002-7Articles167176http://www.medicalimagingandgraphics.com/article/PIIS0895611109001578/abstract?rss=yesAcknowledgement to Referees10.1016/j.compmedimag.2009.12.007Computerized Medical Imaging and Graphics 34, 2 (2010)2010-03-01Computerized Medical Imaging and Graphics2010-03-01342S0895-6111(10)X0002-7Acknowledgement to Referees177178