A tool for handling uncertainty in segmenting regions of interest in medical images. (2nd June 2006)

• Record Type: Journal Article
• Authors: Kontos, Despina; Wang, Qiang; Megalooikonomou, Vasileios; Maurer, Alan H.; Knight, Linda; Kantor, Steve; Simonian, Hrair; Parkman, Henry
• Is Part Of: International journal of intelligent systems technologies and applications. Volume 1:Number 3/4(2006)
• Page Start: 194
• Page End: 210
• Publication Date: 2006-06-02
• Subjects: medical images -- image analysis -- regions of interest -- ROIs -- uncertainty -- region segmentation -- image processing -- fuzzy-connectedness -- software tools; Artificial intelligence -- Periodicals; Intelligent control systems -- Periodicals; 006.3
• Languages: English
• ISSNs: 1740-8865

Abstract

We have developed intelligent software tools for handling the uncertainty in delineating the boundaries of complex structures when segmenting regions of interest (ROIs) in medical images. The focus is on efficiently delineating the boundary of complex 3D organ structures, enabling accurate measurement of their structural and physiologic properties. We employ intensity based thresholding algorithms for interactive and semi-automated analysis. We also explore fuzzy-connectedness concepts in order to deal with the uncertainty in identifying organ surrounding tissue and fully automate the segmentation process. We apply the proposed tools to 3D single-photon emission computed tomography (SPECT) images visualising gastric accommodation and emptying and compare their performance to that of the manual segmentation performed by a human expert. We show that the proposed tools achieve highly accurate delineation of the complex three-dimensional gastric boundaries shown in 3D SPECT images. We also demonstrate their ability to obtain accurate volume calculations based on the segmentation procedure, in order to quantitatively assess organ functional properties such as measuring the gastric mass variation.