Abstract—The researches have shown that incorrect analysis of images is often the result of improper segmentation. Because some troubles like the need to exact definition of Region of Interest (ROI), complex visual characteristics of diseases and difficulty of basic knowledge provision complicate the segmentation step in medical image analysis. Furthermore the segmentation methods are subject to the dimensionality and the modality of imaging. These are owing to a high dependency on factors such as disease type and ROI features. Consequently these reasons lead to remain the segmentation a challengeable and an interesting research field as well. Thus the number of literatures increases annually shown schematically in this paper. In this paper, some problems and applications of segmentation in medical imaging are summarized first and then the progress carrier is shown and proved with some charts in the eleven recent years by searching “image”, “segment” and “medical” phrases on PUBMED, IEEE and Elsevier libraries.
Index Terms—Medical image analysis, segmentation, artificial intelligence, number of literatures, problem, application.
Maryam Rastgarpour is with the Computer Department, Faculty of Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran (e-mail: firstname.lastname@example.org, email@example.com).
Dr. Jamshid Shanbehzadeh is with the Department of Computer Engineering at Kharazmi University (Tarbiat Moallem University of Teheran), Tehran, Iran (e-mail: firstname.lastname@example.org, email@example.com ).
Cite: Maryam Rastgarpour and Jamshid Shanbehzadeh, "The Problems, Applications and Growing Interest in Automatic Segmentation of Medical Images from the Year 2000 till 2011," International Journal of Computer Theory and Engineering vol. 5, no. 1, pp. 1-4, 2013.