AI Article Synopsis
- Collagen Proportional Area (CPA) extraction using digital image analysis (DIA) is an effective method for estimating liver disease staging by accurately representing fibrosis in liver tissue.
- This paper introduces an automated method that uses k-means clustering to detect liver tissue in biopsies, helping to identify necessary repeat biopsies for inadequate samples.
- The proposed method was tested on 25 images, showing only minor variations in CPA calculations compared to expert evaluations, indicating its reliability.
Article Abstract
Collagen Proportional Area (CPA) extraction using digital image analysis (DIA) in liver biopsies provides an effective way to estimate the liver disease staging. CPA represents accurately fibrosis expansion in liver tissue. This paper presents an automated clustering-based method for fibrosis detection and CPA computation. Initially, a k-means based approach is employed to detect the liver tissue and eliminate the background. Next, the method decides about the adequacy of current biopsy, according to the size of liver tissue. Biopsies which contain small and segmented specimens must be repeated. Since the tissue has been detected, fibrosis areas are also found in the tissue. Finally, CPA is computed. For the evaluation of the proposed method 25 images are employed and the percentage errors of CPA are computed for each image. In the majority of the cases, small variation of CPA is computed, comparing to the expert's annotation.
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| http://dx.doi.org/10.1109/EMBC.2015.7319047 | DOI Listing |
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