Computerized Medical Imaging and Graphics This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

In Vivo MRI based Prostate Cancer Localization with Random Forests and Auto-context Model
Prostate cancer is the most commonly diagnosed non-skin cancer and the second leading cause of cancer death among U.S. men [1]. Current clinical practice for the diagnosis of prostate cancer is often based on a transrectal ultrasound (TRUS) biopsy, after the patient shows an elevated serum prostate specific antigen (PSA) level. A large screening trial using PSA and TRUS has shown that it is possible to reduce prostate cancer mortality by 20-30% [2]. However, these studies have also shown that PSA testing in combination with TRUS biopsies has a relatively low specificity. (Source: Computerized Medical Imaging and Graphics)
Source: Computerized Medical Imaging and Graphics - February 27, 2016 Category: Radiology Authors: Chunjun Qian, Li Wang, Yaozong Gao, Ambereen Yousuf, Xiaoping Yang, Aytekin Oto, Dinggang Shen Source Type: research

MRI based Prostate Cancer Localization with Random Forests and Auto-context Model
Prostate cancer is the most commonly diagnosed non-skin cancer and the second leading cause of cancer death among U.S. men [1]. Current clinical practice for the diagnosis of prostate cancer is often based on a transrectal ultrasound (TRUS) biopsy, after the patient shows an elevated serum prostate specific antigen (PSA) level. A large screening trial using PSA and TRUS has shown that it is possible to reduce prostate cancer mortality by 20-30% [2]. However, these studies have also shown that PSA testing in combination with TRUS biopsies has a relatively low specificity. (Source: Computerized Medical Imaging and Graphics)
Source: Computerized Medical Imaging and Graphics - February 27, 2016 Category: Radiology Authors: Chunjun Qian, Li Wang, Yaozong Gao, Ambereen Yousuf, Xiaoping Yang, Aytekin Oto, Dinggang Shen Source Type: research

Curvelet initialized level set cell segmentation for touching cells in low contrast images
The biological cell studies rely on the analysis of large cell clusters with the help of microscopy imaging [1,2]. When the goal is to study phenomena of the live cells, fluorescence microscopy is commonly used as it allows biologists to experiment on the live cells with high sensitivity and specificity. Cell image analysis provides information about the cell characteristics and the dynamic behavior of the cells. The complexity of performing the cell analysis increases in touching cell images. Manual processing of such data is time consuming and error prone, creating a demand for automated techniques [3,4]. (Source: Comput...
Source: Computerized Medical Imaging and Graphics - February 17, 2016 Category: Radiology Authors: Sarabpreet Kaur, J.S. Sahambi Source Type: research

Adapting content-based image retrieval techniques for the semantic annotation of medical images
Medical imaging is a fundamental component of modern healthcare with roles in patient diagnosis, treatment planning, and assessment of response to therapy. A direct consequence of this is the rise in medical imaging informatics research, including content-based image retrieval  [1,2], modality-classification and case-based retrieval  [3], classification  [4,5], and annotation  [5–7]. Semantic image annotation is also emerging as a research question, in which the main research challenge is to detect subtle differences in low-level image features and to relate them to higher-level labels derived from a standard termino...
Source: Computerized Medical Imaging and Graphics - February 4, 2016 Category: Radiology Authors: Ashnil Kumar, Shane Dyer, Jinman Kim, Changyang Li, Philip H.W. Leong, Michael Fulham, Dagan Feng Source Type: research

A Multi-center Milestone Study of Clinical Vertebral CT Segmentation
The vertebral column, also known as spine, is a bony skeletal structure forming the central weight-bearing axis of the human upper body. Multiple medical imaging modalities, such as radiographs, CT, MRI and PET, are used to evaluate spine anatomy and diagnose spinal pathology. Using current generation of scanning techniques, CT is the most spatially accurate modality to assess the three dimensional morphology of the vertebra. Spine segmentation is a fundamental step for most subsequent spine image analysis and modeling tasks, such as identification of spine abnormalities (e.g. (Source: Computerized Medical Imaging and Graphics)
Source: Computerized Medical Imaging and Graphics - January 2, 2016 Category: Radiology Authors: Jianhua Yao, Joseph E. Burns, Daniel Forsberg, Alexander Seitel, Abtin Rasoulian, Purang Abolmaesumi, Kerstin Hammernik, Martin Urschler, Bulat Ibragimov, Robert Korez, Tomaž Vrtovec, Isaac Castro-Mateos, Jose M. Pozo, Alejandro F. Frangi, Ronald M. Summ Source Type: research

A Coronary Artery Segmentation Method Based On MultiScale Analysis And Region Growing
X-ray Coronary Angiography (XCA) is the gold standard for the assessment of clinically significant Coronary Artery Diseases (CAD) [1]. The angiograms obtained by the XCA enable to reveal the initial CAD symptoms by the morphological features of the coronary arteries such as diameter, length, branching angle, and tortuosity. However, complex vessel structure, image noise, poor contrast and non-uniform illumination make vessel tracking a tedious task. Accurate coronary vessel detection is a fundamental step in various medical imaging applications such as stenosis detection [2], 3D reconstruction [3] and cardiac dynamics asse...
Source: Computerized Medical Imaging and Graphics - December 21, 2015 Category: Radiology Authors: Asma Kerkeni, Asma Benabdallah, Antoine Manzanera, Mohamed Hedi Bedoui Source Type: research

System Models for PET Statistical Iterative Reconstruction: a review
Nuclear medicine imaging techniques, whose two major subbranches are single-photon emission computed tomography (SPECT), and positron emission tomography (PET), provide images reflecting physiologic functions unlike other traditional techniques, such as x-ray computed tomography or ultrasound, which yield anatomical structures. In order to image properties of the body's physiology, both the PET and SPECT techniques begin with the injection of a radiopharmaceutical into the subject under study. A radiopharmaceutical consists of a tracer compound that interacts with the body and a radioactive label that, by way of the emissi...
Source: Computerized Medical Imaging and Graphics - December 19, 2015 Category: Radiology Authors: A. Iriarte, R. Marabini, S. Matej, C.O.S. Sorzano, R.M. Lewitt Source Type: research

A novel level set model with automated initialization and controlling parameters for medical image segmentation
In this paper, a level set model without the need of generating initial contour and setting controlling parameters manually is proposed for medical image segmentation. The contribution of this paper is mainly manifested in three points. First, we propose a novel adaptive mean shift clustering method based on global image information to guide the evolution of level set. By simple threshold processing, the results of mean shift clustering can automatically and speedily generate an initial contour of level set evolution. (Source: Computerized Medical Imaging and Graphics)
Source: Computerized Medical Imaging and Graphics - December 19, 2015 Category: Radiology Authors: Qingyi Liu, Mingyan Jiang, Peirui Bai, Guang Yang Source Type: research

Development and Validation of Real-time Simulation of X-ray Imaging with Respiratory Motion
There is a growing need for fast, accurate and validated tools for the virtual physiological human (VPH) in physics, imaging, and simulation in medicine. The aim of VPH is to provide a digital model of the human physiology as a single complex system. This research's contribution is twofold: provide respiration modelling with real-time performance, generate accurate X-ray images from the virtual patient. The output can be exploited in many different contexts. (Source: Computerized Medical Imaging and Graphics)
Source: Computerized Medical Imaging and Graphics - December 17, 2015 Category: Radiology Authors: Franck P. Vidal, Pierre-Frédéric Villard Source Type: research

Uncertainty analysis of quantitative imaging features extracted from contrast-enhanced CT in lung tumors
In recent years, there has been a trend of developing quantitative imaging features or texture features to characterize tumors for the purposes of diagnosis, disease classification, and treatment outcome prediction [1–8]. This kind of research is also known as “Radiomics,” a high-throughput extraction of quantitative imaging features from medical images to create mineable databases for prognostic analysis [9,10]. In Radiomics research, a large number of studies have focused on the texture features extracted from computed tomography (CT) images to predict the treatment outcomes of non-small cell lung cancer [1–3,11]...
Source: Computerized Medical Imaging and Graphics - December 14, 2015 Category: Radiology Authors: Jinzhong Yang, Lifei Zhang, Xenia J. Fave, David V. Fried, Francesco C. Stingo, Chaan S. Ng, Laurence E. Court Source Type: research

An Efficient Level Set Method for Simultaneous Intensity Inhomogeneity Correction and Segmentation of MR Images
Intensity inhomogeneity is a common artefact that often occurs in medical images and is caused by imperfect image acquisition. For example, radio frequency field inhomogeneities appear due to either a non-uniform field itself or a non-uniform sensitivity of the receiver and transmitter coils. Such artefacts lead to undesired intensity variations in the same tissue types across the image. They hinder successful automated image segmentation, especially if the segmentation algorithms rely only on image intensities [15]. (Source: Computerized Medical Imaging and Graphics)
Source: Computerized Medical Imaging and Graphics - December 14, 2015 Category: Radiology Authors: Tatyana Ivanovska, René Laqua, Lei Wang, Andrea Schenk, Jeong Hee Yoon, Katrin Hegenscheid, Henry Völzke, Volkmar Liebscher Source Type: research

NSCLC tumor shrinkage prediction using quantitative image features
A goal of oncology therapies is to utilize information gained from treating previous patients to deliver treatment specific to the patient and disease. The current tumor node metastasis (TNM) staging system for non-small cell lung cancer (NSCLC) is used for this purpose and utilizes anatomical information such as the tumor size, location, spread, and lymph node involvement [1]. Although this system is based on the study of over 67,000 NSCLC cases, NSCLC patients with the same TNM staging often have very different clinical outcomes. (Source: Computerized Medical Imaging and Graphics)
Source: Computerized Medical Imaging and Graphics - November 28, 2015 Category: Radiology Authors: Luke A. Hunter, Yi Pei Chen, Lifei Zhang, Jason E. Matney, Haesun Choi, Stephen F. Kry, Mary K. Martel, Francesco Stingo, Zhongxing Liao, Daniel Gomez, Jinzhong Yang, Laurence E. Court Source Type: research

3D Surface-based Registration of Ultrasound and Histology in Prostate Cancer Imaging
Prostate cancer (PCa) is the type of cancer with the highest incidence and second highest mortality among males in the United States [1]. Despite the statistics of this cancer type, the main diagnostic technique, systematic biopsy, has major drawbacks. Firstly, being invasive, it can cause infections and hematuria [2]. Secondly, tumors can be missed by the biopsy needle [3], resulting in poor sensitivity of this diagnostic tool. Thirdly, tumors can be undergraded when the more aggressive region of a tumor is missed [4], leading to undertreatment. (Source: Computerized Medical Imaging and Graphics)
Source: Computerized Medical Imaging and Graphics - November 16, 2015 Category: Radiology Authors: Stefan G. Schalk, Arnoud Postema, Tamerlan A. Saidov, Libertario Demi, Martijn Smeenge, Jean J.M.C.H. de la Rosette, Hessel Wijkstra, Massimo Mischi Source Type: research

An Approach to Locate Optic Disc in Retinal Images with Pathological Changes
The detection of retinal structure is an important prerequisite to diagnose retinal diseases automatically. The retina contains several important structures, such as optic disc (OD), fovea, and vessels. Optic disc is the region where blood vessels and optic nerves converge. There are no light-sensing cells in OD area, so optic disc is also called blind spot. In the normal retinal images, OD is the brightest part and looks like a pale, nearly circular or vertically slightly oval disk. Correct OD detection is the basic step for computer aided diagnosis of different eye diseases, such as diabetic retinopathy [1] and glaucoma ...
Source: Computerized Medical Imaging and Graphics - November 13, 2015 Category: Radiology Authors: Li Xiong, Huiqi Li Source Type: research

Automatic tracking of vessel-like structures from a single starting point
The identification of vascular networks is an important topic in the medical image analysis community. While most methods focus on single vessel tracking, the few solutions that exist for tracking complete vascular networks are usually computationally intensive and require a lot of user interaction. In this paper we present a method to track full vascular networks iteratively using a single starting point. Our approach is based on a cloud of sampling points distributed over concentric spherical layers. (Source: Computerized Medical Imaging and Graphics)
Source: Computerized Medical Imaging and Graphics - November 12, 2015 Category: Radiology Authors: Dário Augusto Borges Oliveira, Laura Leal-Taixé, Raul Queiroz Feitosa, Bodo Rosenhahn Source Type: research