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Automatic Liver Segmentation Method on MR Images using Normalized Gradient Magnitude Image  

Lee, Jeong-Jin (가톨릭대학교 디지털미디어학부)
Kim, Kyoung-Won (울산대학교 의과대학 영상의학과)
Lee, Ho (스탠포드대학교 방사선종양학과)
Publication Information
Journal of Korea Multimedia Society / v.13, no.11, 2010 , pp. 1698-1705 More about this Journal
Abstract
In this paper, we propose a fast liver segmentation method from magnetic resonance(MR) images. Our method efficiently divides a MR image into a set of discrete objects, and boundaries based on the normalized gradient magnitude information. Then, the objects belonging to the liver are detected by using 2D seeded region growing with seed points, which are extracted from the segmented liver region of the slice immediately above or below the current slice. Finally, rolling ball algorithm, and connected component analysis minimizes false positive error near the liver boundaries. Our method was validated by twenty data sets and the results were compared with the manually segmented result. The average volumetric overlap error was 5.2%, and average absolute volumetric measurement error was 1.9%. The average processing time for segmenting one data set was about three seconds. Our method could be used for computer-aided liver diagnosis, which requires a fast and accurate segmentation of liver.
Keywords
Liver Segmentation; Normalized Gradient Magnitude Image; Rolling Ball Algorithm; MR Images;
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