An Adaptive Unimodal and Hysteresis Thresholding Method

Yu, Yin; Li, Zhen; Liu, Bing; Liu, Xiangdong

doi:10.1007/978-3-662-45049-9_90

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…To address this problem, we first convert each rotated tissue image to gray scale and enhance its contrast with adaptive histogram equalization [13] method. Next, we use the hysteresis thresholding to binarize the gray scale image with low and high threshold values as 0.65 and 0.8, respectively [15]. For the remaining tissue regions, we use morphological operations to smooth the boundary of steatosis components and to remove small objects that are too small to be steatosis in the post-processing step.…”

Section: B Quantification Of Steatosis Componentsmentioning

confidence: 99%

Segmentation of Overlapped Steatosis in Whole-Slide Liver Histopathology Microscopy Images

Roy

Wang

Teodoro

et al. 2018

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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An accurate steatosis quantification with pathology tissue samples is of high clinical importance. However, such pathology measurement is manually made in most clinical practices, subject to severe reader variability due to large sampling bias and poor reproducibility. Although some computerized automated methods are developed to quantify the steatosis regions, they present limited analysis capacity for high resolution whole-slide microscopy images and accurate overlapped steatosis division. In this paper, we propose a method that extracts an individual whole tissue piece at high resolution with minimum background area by estimating tissue bounding box and rotation angle. This is followed by the segmentation and segregation of steatosis regions with high curvature point detection and an ellipse fitting quality assessment method. We validate our method with isolated and overlapped steatosis regions in liver tissue images of 11 patients. The experimental results suggest that our method is promising for enhanced support of steatosis quantization during the pathology review for liver disease treatment.

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Section: B Quantification Of Steatosis Componentsmentioning

confidence: 99%