A semi-automated method for liver tumor segmentation based on 2D region growing with
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Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/1428
Liver tumour segmentation from computed tomography (CT) scans is a challenging task. A semi-automatic method based on 2D region growing with knowledge-based constraints is proposed to segment lesions from constituent 2D slices obtained from 3D CT images. Minimal user involvement is required to define an approximate region of interest around the suspected legion area. The seed point and feature vectors are then calculated and voxels are labeled using a region-growing approach. Knowledge-based constraints are incorporated into the method to ensure the size and shape of the segmented region is within acceptable parameters. The individual segmented lesions can then be stacked together to generate a 3D volume. The proposed method was tested on a training set of 10 tumours and a testing set of 10 tumours. To evaluate the results quantitatively, various measures were used to generate scores. Based on the results obtained from the 10 testing tumours, the method was resulted in an average score of 64.

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plus my review by Xiang Deng on 07-25-2008 for revision #2
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Categories: Image, Region growing, Segmentation, Unsupervised learning and clustering
Keywords: liver tumor, segmentation, region-growing
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