Tumor segmentation from computed tomography image data using a probabilistic pixel selection approach

Authors:
Jung Leng Foo;Go Miyano;Thom Lobe;Eliot Winer
Affiliations:
1620 Howe Hall, Virtual Reality Applications Center, Iowa State University, Ames, IA 50011, USA;Pediatric General and Urogenital Surgery Department, Juntendo University School of Medicine, Tokyo, Japan;Pediatric Surgeon, Pediatric Surgery Department, Blank's Children Hospital, Des Moines, IA, USA;Mechanical Engineering and Human-Computer Interaction Department, 1620 Howe Hall, Virtual Reality Applications Center, Iowa State University, Ames, IA 50011, USA
Venue:
Computers in Biology and Medicine
Year:
2011

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Abstract

Automatic segmentation of tumors is a complicated and difficult process as most tumors are rarely clearly delineated from healthy tissues. A new method for probabilistic segmentation to efficiently segment tumors within CT data and to improve the use of digital medical data in diagnosis has been developed. Image data are first enhanced by manually setting the appropriate window center and width, and if needed a sharpening or noise removal filter is applied. To initialize the segmentation process, a user places a seed point within the object of interest and defines a search region for segmentation. Based on the pixels' spatial and intensity properties, a probabilistic selection criterion is used to extract pixels with a high probability of belonging to the object. To facilitate the segmentation of multiple slices, an automatic seed selection algorithm was developed to keep the seeds in the object as its shape and/or location changes between consecutive slices. The seed selection algorithm performs a greedy search by searching for pixels with matching intensity close to the location of the original seed point. A total of ten CT datasets were used as test cases, each with varying difficulty in terms of automatic segmentation. Five test cases had mean false positive error rates less than 10%, and four test cases had mean false negative error rates less than 10% when compared to manual segmentation of those tumors by radiologists.