An effective segmentation approach for liver computed tomography scans using fuzzy exponential entropy

Elaraby A.E., Nechaevskiy A.V.
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Accurate segmentation of liver plays important in contouring during diagnosis and the planning of treatment. Imaging technology analysis and processing are wide usage in medical diagnostics, and therapeutic applications. Liver segmentation referring to the process of automatic or semi-automatic detection of liver image boundaries. A major difficulty in segmentation of liver image is the high variability as; the human anatomy itself shows major variation modes. In this paper, a proposed approach for computed tomography (CT) liver segmentation is presented by combining exponential entropy and fuzzy c-partition. Entropy concept has been utilized in various applications in imaging computing domain. Threshold techniques based on entropy have attracted a considerable attention over the last years in image analysis and processing literatures and it is among the most powerful techniques in image segmentation. In the proposed approach, the computed tomography (CT) of liver is transformed into fuzzy domain and fuzzy entropies are defined for liver image object and background. In threshold selection procedure, the proposed approach considers not only the information of liver image background and object, but also interactions between them as the selection of threshold is done by find a proper parameter combination of membership function such that the total fuzzy exponential entropy is maximized. Differential Evolution (DE) algorithm is utilizing to optimize the exponential entropy measure to obtain image thresholds. Experimental results in different CT livers scan are done and the results demonstrate the efficient of the proposed approach. Based on the visual clarity of segmented images with varied threshold values using the proposed approach, it was observed that liver segmented image visual quality is better with the results higher level of threshold.

Keywords: segmentation, liver CT, threshold, fuzzy exponential entropy, differential evolution
Citation in English: Elaraby A.E., Nechaevskiy A.V. An effective segmentation approach for liver computed tomography scans using fuzzy exponential entropy // Computer Research and Modeling, 2021, vol. 13, no. 1, pp. 195-202
Citation in English: Elaraby A.E., Nechaevskiy A.V. An effective segmentation approach for liver computed tomography scans using fuzzy exponential entropy // Computer Research and Modeling, 2021, vol. 13, no. 1, pp. 195-202
DOI: 10.20537/2076-7633-2021-13-1-195-202
URL: http://crm-en.ics.org.ru/journal/article/3036/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

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