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Proceedings Paper

Design of a benchmark dataset, similarity metrics, and tools for liver segmentation
Author(s): Suryaprakash Kompalli; Mohammed Alam; Raja S. Alomari; Stanley T. Lau; Vipin Chaudhary
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Paper Abstract

Reliable segmentation of the liver has been acknowledged as a significant step in several computational and diagnostic processes. While several methods have been designed for liver segmentation, comparative analysis of reported methods is limited by the unavailability of annotated datasets of the abdominal area. Currently available generic data-sets constitute a small sample set, and most academic work utilizes closed datasets. We have collected a dataset containing abdominal CT scans of 50 patients, with coordinates for the liver boundary. The dataset will be publicly distributed free of cost with software to provide similarity metrics, and a liver segmentation technique that uses Markov Random Fields and Active Contours. In this paper we discuss our data collection methodology, implementation of similarity metrics, and the liver segmentation algorithm.

Paper Details

Date Published: 27 March 2008
PDF: 8 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 691537 (27 March 2008); doi: 10.1117/12.772940
Show Author Affiliations
Suryaprakash Kompalli, Univ. at Buffalo (United States)
Mohammed Alam, Wayne State Univ. (United States)
Raja S. Alomari, Univ. at Buffalo (United States)
Stanley T. Lau, Women and Children's Hospital of Buffalo (United States)
Vipin Chaudhary, Univ. at Buffalo (United States)


Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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