Automatic segmentation of the liver in CT images using the watershed algorithm based on morphological filtering

Seong-Jae Lim; Yong-Yeon Jeong; Chil-Woo Lee; Yo-Sung Ho

doi:10.1117/12.533586

12 May 2004 Automatic segmentation of the liver in CT images using the watershed algorithm based on morphological filtering

Seong-Jae Lim, Yong-Yeon Jeong, Chil-Woo Lee, Yo-Sung Ho

Author Affiliations +

Proceedings Volume 5370, Medical Imaging 2004: Image Processing; (2004) https://doi.org/10.1117/12.533586
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

Liver segmentation is one of the most basic and important parts in computer-aided diagnosis for liver CT. Although various segmentation methods have been proposed for medical imaging, most of them generally do not perform well in segmenting the liver from CT images because of surface features of the liver and difficulty of discrimination from other adjacent organs. In this paper, we propose a new scheme for automatic segmentation of the liver in CT images. The pro-posed scheme is carried out on region-of-interest (ROI) blocks that include regions of the liver with high probabilities. The ROI approach saves unnecessary computational loss in finding the accurate boundary of the liver. The proposed method utilizes the composition of morphological filters with a priori knowledge, such as the general location or the approximate intensity of the liver to detect the initial boundary of the liver. Then, we make the gradient image with the weight of an initial liver boundary and segment the liver region by using an immersion-based watershed algorithm in the gradient image. Finally, a refining process is carried out to acquire a more accurate liver region.

Citation Download Citation

Seong-Jae Lim, Yong-Yeon Jeong, Chil-Woo Lee, and Yo-Sung Ho "Automatic segmentation of the liver in CT images using the watershed algorithm based on morphological filtering", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); https://doi.org/10.1117/12.533586

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