Automatic Single-Organ Segmentation in Computed Tomography Images

Authors:
Ruchaneewan Susomboon;Daniela Raicu;Jacob Furst;David Channin
Affiliations:
DePaul University, USA;DePaul University, USA;DePaul University, USA;Northwestern University, USA
Venue:
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Year:
2006

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Integrated region-based segmentation using color components and texture features with prior shape knowledge

International Journal of Applied Mathematics and Computer Science

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Abstract

In this paper, we propose a hybrid approach for automatic single-organ segmentation in Computed Tomography (CT) data. The approach consists of three stages: first, a probability image of the organ of interest is obtained by applying a binary classification model obtained using pixel-based texture features; second, an adaptive split-and-merge segmentation algorithm is applied on the organ probability image to remove the noise introduced by the misclassified pixels; and third, the segmented organ's boundaries from the previous stage are iteratively refined using a region growing algorithm. While we applied our approach for liver segmentation in 2-D CT images, a challenging and important task in many medical applications, the proposed approach can be applied for the segmentation of any other organ in CT images. Moreover, the proposed approach can be extended to perform automatic multiple organ segmentation and to build context-sensitive reporting tools for computer-aided diagnosis applications.