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    Detection of fibrosis in liver biopsy images using multi-objective genetic programming
    (2017-07-01)
    Thong-On, Purit
    ;
    Watchareeruetai, Ukrit
    This paper proposes an automatic construction of feature extractor for liver fibrosis detection using a multiobjective genetic programming approach in which a constructed feature extractor was measured in different aspects in which becomes the objectives of the evolutionary run. The result of the evolutionary run is a set of solutions with different strengths and weaknesses. A solution from each experiment is selected and compared with a benchmark handcraft method in by each experiment and top-five manners. One of the best result obtained has 2.09 fibrosis estimation error which is less than the benchmark method with 2.63 fibrosis estimation error.
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    Detection of fibrosis in liver biopsy images by using Bayesian classifier
    (2015-02-27)
    Meejaroen, Kanyanat
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    Chaweechan, Charoen
    ;
    Khodsiri, Wanus
    ;
    Khu-Smith, Vorapranee
    ;
    Watchareeruetai, Ukrit
    In this paper, an image-processing-based method designed to detect fibrosis in liver biopsy images is proposed. The proposed method first enhances the color difference between liver tissue and fibrosis areas. Then, a low-pass filtering is applied to each color band to reduce noise. In order to calculate the percentage of fibrosis against total liver tissue, the background area, i.e. empty slide area, is detected. Next, Bayesian classifier is used to separate fibrosis from liver tissue based on the color information. Finally, the proportion of the fibrosis area to the tissue area is computed. Experimental results show that the proposed method can estimate and detect fibrosis in the liver biopsy images with the classification accuracy of 91.42%. In addition, the average difference between the percentage of fibrosis obtained from the proposed method and that in ground truth images is 2.29 points.
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    Fat detection algorithm for liver biopsy images
    (2014-10-15)
    Sumitpaibul, Pawesuda
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    Damrongphithakkul, Anurak
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    Watchareeruetai, Ukrit
    This paper presents an image-processing-based method for analyzing the fat proportion in liver biopsy images. Firstly, the proposed method extracts the area of candidate fat blobs, as well as the background area, from the input image. Then the features of each candidate blobs will be computed. Finally a classification technique called k-nearest neighbors is used to classify each candidate blob if it is fat. Experimental results show that the proposed method can detect fat in the liver biopsy images with the accuracy of 97.52%.