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 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/?rss=yesElsevier Inc.en © 2010 Published by Elsevier Inc. All rights reserved. Computerized Medical Imaging and Graphics0895-6111347October 2010 © 2010 Published by Elsevier Inc. All rights reserved. healthpermissions@elsevier.comhttp://www.medicalimagingandgraphics.com/article/PIIS0895611110000698/abstract?rss=yesEditorial Board10.1016/S0895-6111(10)00069-8Computerized Medical Imaging and Graphics 34, 7 (2010)2010-10-01Computerized Medical Imaging and Graphics2010-10-01347S0895-6111(10)X0007-6CO2CO2http://www.medicalimagingandgraphics.com/article/PIIS0895611110000418/abstract?rss=yesAbstract: An automated lung nodule detection system can help spot lung abnormalities in CT lung images. Lung nodule detection can be achieved using template-based, segmentation-based, and classification-based methods. The existing systems that include a classification component in their structures have demonstrated better performances than their counterparts. Ensemble learners combine decisions of multiple classifiers to form an integrated output. To improve the performance of automated lung nodule detection, an ensemble classification aided by clustering (CAC) method is proposed. The method takes advantage of the random forest algorithm and offers a structure for a hybrid random forest based lung nodule classification aided by clustering. Several experiments are carried out involving the proposed method as well as two other existing methods. The parameters of the classifiers are varied to identify the best performing classifiers. The experiments are conducted using lung scans of 32 patients including 5721 images within which nodule locations are marked by expert radiologists. Overall, the best sensitivity of 98.33% and specificity of 97.11% have been recorded for proposed system. Also, a high receiver operating characteristic (ROC) Az of 0.9786 has been achieved.Random forest based lung nodule classification aided by clusteringS.L.A. Lee, A.Z. Kouzani, E.J. Hu10.1016/j.compmedimag.2010.03.006Computerized Medical Imaging and Graphics 34, 7 (2010)2010-04-30Computerized Medical Imaging and Graphics2010-04-30347S0895-6111(10)X0007-6Articles535542http://www.medicalimagingandgraphics.com/article/PIIS0895611110000406/abstract?rss=yesAbstract: This article illustrates a new method to align and merge two partially overlapping volumes each of them generated by cone beam computed tomography (CBCT). The aggregate volume covers a larger area of investigation and is determined by localizing one fixed LEGO brick in both of the primal volumes. Based on the LEGO brick an approximate registration of the volumes is determined. Afterwards we improve the transformation by minimizing the difference in overlapping space. In this paper we present a method which automates these two steps and provides an aligned volume.Alignment of cone beam computed tomography data using intra-oral fiducial markersDan Brüllmann, Martin Seelge, Elmar Schömer, Ralf Schulze, Ulrich Schwanecke10.1016/j.compmedimag.2010.03.005Computerized Medical Imaging and Graphics 34, 7 (2010)2010-04-26Computerized Medical Imaging and Graphics2010-04-26347S0895-6111(10)X0007-6Articles543552http://www.medicalimagingandgraphics.com/article/PIIS089561111000039X/abstract?rss=yesAbstract: This paper addresses the problem of estimating the 3D rigid poses of a CT volume of an object from its 2D X-ray projection(s). We use maximization of mutual information, an accurate similarity measure for multi-modal and mono-modal image registration tasks. However, it is known that the standard mutual information measures only take intensity values into account without considering spatial information and their robustness is questionable. In this paper, instead of directly maximizing mutual information, we propose to use a variational approximation derived from the Kullback-Leibler bound. Spatial information is then incorporated into this variational approximation using a Markov random field model. The newly derived similarity measure has a least-squares form and can be effectively minimized by a multi-resolution Levenberg-Marquardt optimizer. Experiments were conducted on datasets from two applications: (a) intra-operative patient pose estimation from a limited number (e.g. 2) of calibrated fluoroscopic images, and (b) post-operative cup orientation estimation from a single standard X-ray radiograph with/without gonadal shielding. The experiment on intra-operative patient pose estimation showed a mean target registration accuracy of 0.8mm and a capture range of 11.5mm, while the experiment on estimating the post-operative cup orientation from a single X-ray radiograph showed a mean accuracy below 2° for both anteversion and inclination. More importantly, results from both experiments demonstrated that the newly derived similarity measures were robust to occlusions in the X-ray image(s).Effective incorporating spatial information in a mutual information based 3D–2D registration of a CT volume to X-ray imagesGuoyan Zheng10.1016/j.compmedimag.2010.03.004Computerized Medical Imaging and Graphics 34, 7 (2010)2010-04-23Computerized Medical Imaging and Graphics2010-04-23347S0895-6111(10)X0007-6Articles553562http://www.medicalimagingandgraphics.com/article/PIIS0895611110000388/abstract?rss=yesAbstract: Physicians evaluate computed tomography (CT) of the brain to quantitatively and qualitatively identify various types of intracranial hematomas for patients with neurological emergencies. We propose a novel method that can perform this task in a totally automatic fashion, based on a multiresolution binary level set method. The skull regions are segmented in downsized images generated with a maximum filter. The intracranial regions are located using the average gray levels and connectivity. These regions compose the regions of interest (ROIs) for segmenting the hematoma from the normal brain. The gray levels of the voxels within these ROIs are generated with an averaging filter in a multiresolution fashion. After identifying the candidate hematoma voxels using adaptive thresholds and connectivity, binary level set algorithm is applied repeatedly until the original resolution is reached. We apply our method to non-volumetric non-contrast CT images of 15 surgically proven intracranial hematomas and the results were quantitatively evaluated by a human expert. The correlation coefficient between the volumes measured manually and automatically is 0.97. The overlap metrics ranged from 0.97 to 0.74, with an average of 0.88. The average precision and recall are 0.89 and 0.87, respectively. We use decision rules to classify these hematomas and were able to make correct diagnoses in all cases.Computer-aided diagnosis of intracranial hematoma with brain deformation on computed tomographyChun-Chih Liao, Furen Xiao, Jau-Min Wong, I.-Jen Chiang10.1016/j.compmedimag.2010.03.003Computerized Medical Imaging and Graphics 34, 7 (2010)2010-04-26Computerized Medical Imaging and Graphics2010-04-26347S0895-6111(10)X0007-6Articles563571http://www.medicalimagingandgraphics.com/article/PIIS0895611110000285/abstract?rss=yesAbstract: Developing detailed lung airway models is an important step towards understanding the respiratory system. While modern imaging and airway casting approaches have dramatically improved the potential detail of such models, challenges have arisen in image processing as the demand for greater detail pushes the image processing approaches to their limits. Airway segmentations with proper topology have neither loops nor invalid voxel-to-voxel connections. Here we describe a new technique for segmenting airways with proper topology and apply the approach to an image volume generated by magnetic resonance imaging of a silicone cast created from an excised monkey lung.High resolution lung airway cast segmentation with proper topology suitable for computational fluid dynamic simulationsJames P. Carson, Daniel R. Einstein, Kevin R. Minard, Michelle V. Fanucchi, Christopher D. Wallis, Richard A. Corley10.1016/j.compmedimag.2010.03.001Computerized Medical Imaging and Graphics 34, 7 (2010)2010-04-12Computerized Medical Imaging and Graphics2010-04-12347S0895-6111(10)X0007-6Articles572578http://www.medicalimagingandgraphics.com/article/PIIS0895611110000273/abstract?rss=yesAbstract: Cystoscopy is used as a reference clinical examination in the detection and visualization of pathological bladder lesions. Evolution observation and analysis of these lesions is easier when panoramic images from internal bladder walls are used instead of video sequences. This work describes a fast and automatic mosaicing algorithm applied to cystoscopic video sequences, where perspective geometric transformations link successive image pairs. This mosaicing algorithm begins with a fast initialization of translation parameters computed by a cross-correlation of images, followed by an iterative optimization of transformation parameters. Finally, registered images are projected onto a global common coordinate system. A quantifying test protocol applied over a phantom yielded a mosaicing mean error lower than 4pixels for a pixels panoramic image. Qualitative evaluation of 10 panoramic images resulting from videos of clinical cystoscopies was performed. An analysis performed over translation values from these clinical sequences (in vivo) is used to modify the mosaicing algorithm to be able to do a dynamic selection of image pairs. Construction time of panoramic images takes some minutes. At last, algorithm limits are discussed.Fast construction of panoramic images for cystoscopic explorationY. Hernández-Mier, W.C.P.M. Blondel, C. Daul, D. Wolf, François Guillemin10.1016/j.compmedimag.2010.02.002Computerized Medical Imaging and Graphics 34, 7 (2010)2010-10-01Computerized Medical Imaging and Graphics2010-10-01347S0895-6111(10)X0007-6Articles579592