A clustering based method for collagen proportional area extraction in liver biopsy images

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.

Quantification of Liver Fibrosis—A Comparative Study

“…In recent years, the employment of machine learning techniques has flourished as a necessary computer vision tool, but the published methodologies [55][56][57][58][59] were evaluated using a relatively limited dataset (8-79 biopsy specimens). This is due to the fact that the semi-quantitative assessment and the annotation of various histological structures is a time-consuming but also a necessary process for the employment of each method.…”

“…In Giannakeas et al [57], an automated diagnostic method based on clustering was proposed for the extraction of the image of the tissue and the detection of fibrosis in CHC biopsy specimens. Clustering refers to a more sophisticated process of image segmentation, as opposed to thresholding techniques that were used for separating the background from the tissue pixels, as well as for the detection of fibrosis pixels in liver tissue.…”

“…Giannakeas et al [57] and Tsipouras et al [58] insisted on the selection of the picro-Sirius red dye for CPA extraction. Yu et al [61], in addition to the use of SHG microscopy to non-stained TAA-induced fibrotic images, followed the direction of picro-Sirius red liver samples analysis by an expert pathologist.…”

“…All images in Giannakeas et al [57] were characterized according to the Ishak HAI, while each image was annotated at a pixel level by a specialist physician. For quantifying the performance of the proposed method, the extracted CPA values were compared to the annotated ones.…”

“…In Giannakeas et al [57], the application of the K-means algorithm was proven to be more beneficial than thresholding, due to the use of all three channels of the color image. More specifically, the pixel intensity from each channel was used as the feature vector for clustering and overall, the pixel color information was included in the segmentation procedure.…”