Detection of fibrosis in liver biopsy images by using Bayesian classifier
Detection of fibrosis in liver biopsy images by using Bayesian classifier
复制标题
使用贝叶斯分类器检测肝活检图像中的纤维化
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Taya Kittiyakara
中科院分区：
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作者：
Kanyanat Meejaroen;Charoen Chaweechan;Wanus Khodsiri;Vorapranee Khu;U. Watchareeruetai;Pattana Sornmagura;Taya Kittiyakara
本文提出了一种基于图像处理的肝活检图像纤维化检测方法。该方法首先增强了肝组织和纤维化区域之间的色差。然后，对每个颜色带进行低通滤波以降低噪声。为了计算肝纤维化占总肝组织的百分比，检测背景区域，即空切片区域。其次，利用贝叶斯分类器根据颜色信息对肝组织进行纤维化分离。最后计算出纤维化面积占组织面积的比例。实验结果表明，该方法可以估计和检测肝活检图像中的纤维化，分类准确率为91.42%。此外，该方法获得的纤维化百分比与地面真实图像的平均差异为2.29点。
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.