Work place: Department of Mechanical Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
E-mail: haghipour@iaut.ac.ir
Website:
Research Interests: Data Structures and Algorithms, Image Processing, Systems Architecture, Artificial Intelligence, Bioinformatics
Biography
Siyamak Haghipour was born in Urmia, Iran in 1974. He received his bachelor degree from Urmia University, Iran, in 1996, in Electrical Engineering (Major Option in Communication Systems). He got his MSc degree from Iran University of Science & Technology, Iran, in 1999 and Ph.D. degree from Islamic Azad University, Science and Research Branch, Iran, in 2006 all in BiomedicalBioelectric Engineering. His basic research is about Signal & Image Processing, Artificial Intelligence, Bioinformatics and Biological Systems Modeling.
By Maryam Aghai Amirkhizi Siyamak Haghipour
DOI: https://doi.org/10.5815/ijigsp.2013.10.03, Pub. Date: 8 Aug. 2013
Desired segmentation of the image is a pivotal problem in image processing. Segmenting the left ventricle (LV) in magnetic resonance images (MRIs) is essential for evaluation of cardiac function. For the segmentation of cardiac MRI several methods have been proposed and implemented. Each of them has advantages and restrictions. A modified region-based active contour model was applied for segmentation of LV chamber. A new semi-automatic algorithm was suggested calculating the appropriate Balloon force according to mean intensity of the region of interest for each image. The database is included of 2,039 MR images collected from 18 children under 18. The results were compared with previous literatures according to two standards: Dice Metric (DM) and Point to Curve (P2C). The obtained segmentation results are better than previously reported values in several literatures. In this study different points were used in cardiac cycle and several slice levels and classified into three levels: Base, Mid. and Apex. The best results were obtained at end diastole (ED) in comparison with end systole (ES), and on base slice than other slices, because of LV bigger size in ED phase and base slice. With segmentation of LV MRI based on novel active contour and application of the suggested algorithm for balloon force calculation, the mean improvement of DM compared to Grosgeorge et al. is 19.6% in ED and 49.5% in ES phase. The mean improvement of P2C compared with the same literature respectively for ED and ES phase is 43.8% and 39.6%.
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