Classification of Image Processing Software Tools for Cardiovascular Image Analysis
Cardiovascular disease (CVD) is one of the leading causes of death in the modern world. Cardiac image analysis data is crucial in biomedical modelling and simulation to predict and diagnose CVD. Despite the importance of cardiovascular image analysis (CVI
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Department of Software Engineering, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia 2 Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia
Abstract— Cardiovascular disease (CVD) is one of the leading causes of death in the modern world. Cardiac image analysis data is crucial in biomedical modelling and simulation to predict and diagnose CVD. Despite the importance of cardiovascular image analysis (CVIA) software tools for prediction, diagnosis, and therapy of CVD, to the best of our knowledge, there is no comprehensive review and a classification framework for the CVIA tools. In this paper, we review the literature related to the application of software tools for CVIA. In order to determine how image processing software tools are used for the CVIA diagnosis and prediction, this paper reviews the applications and features of these tools, through a survey of literature and the classification of articles, from January 2005 to December 2014. Keyword indices and article abstracts were used to identify 86 articles concerning image processing software tools for the CVIA. In this research, we review and classify 66 identified software tools for CVIA, with respect to the following four areas: (1) supported cardiovascular application, (2) cardiovascular imaging dimensionality, (3) cardiovascular imaging modality, and (4) post-processing ability. The results of our review and classification provide guidelines for utilization of CVIA software tool and future research on them. Keywords— medical image processing, software tool, cardiovascular image analysis, cardiovascular disease.
I. INTRODUCTION CVD is reported as the one major cause of death globally [5]. Applied medical research highly depends on the diagnosis and outcome prediction of cardiac images which contributed to the growing number of CVIA tools in the market. Researchers often sought these tools to enlighten the tasks from eliciting useful diagnostics information investigate the gist of a cardiovascular (CV) image to constructing patient-specific simulation models. Examples of CVIA software tools are Segment [1-2] and Slicer or 3D Slicer [3]. Over the last decade, biomedical researchers and experts have employed and developed various CVIA software tools to meet their requirements. Although there are many CVIA
tools available, the lack of classification scheme for these tools somewhat hinders its wide usage. As each tool comes with different functionality and applicability, biomedical researchers and experts find it hard to choose an appropriate one among the various tools which fits the purpose of their use. Inappropriate selection of CVIA tools can cause major effect on the analysis and prediction of CVD. Apart from lacking classification, the process of comparing and selecting a tool from the abundant number of tools itself, is a difficult process. Biomedical researchers may not possess sufficient technical know
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