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Liver Tumor Localization Using an Integrated Hybrid Fuzzy Seed Point Region Growing Algorithm |
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PP: 371-380 |
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doi:10.18576/amis/13S140
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Author(s) |
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Shaimaa A. Elmorsy,
Mohamed A.Abdou,
Yasser F.Hassan,
Ashraf Elsayed,
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Abstract |
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| Image processing is one of the most important fields to replace non-invasive diagnosis in Hepatocellular Carcinoma (HCC). Liver segmentation is one of the main processes in the detection of many liver diseases non-invasively. Accurate liver tumor segmentation is of great importance in tumor detection and localization. This paper proposes an integrated hybrid fully automatic method for tumor segmentation for abdominal 2D CT scans together with the detection of tumor main parameter (size, percentage to the liver and safe area). The proposed FSPRG method uses the Fuzzy C-means method as a first step, followed by a complementary region-growing method that builds its knowledge on the Fuzzy C- means stage. After tumor segmentation process finishes the main parameters of tumor are calculated using Laplacian of Gaussian (LOG) curve. The whole process is applied to a set of 2D CT of primary diagnosed HCC patients and the proposed algorithm. Results show that the proposed integrated method succeeds in segmenting the tumor region from the liver.
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