Computational method for unsupervised segmentation of lymphoma histological images based on fuzzy 3-partition entropy and genetic algorithm

Abstract

a b s t r a c tNon-Hodgkin lymphoma is the most common cancer of the lymphatic system and should be considered as a group of several closely related cancers, which can show differences in their growth patterns, their impact on the body and how they are treated. The diagnosis of the different types of neoplasia is made by a specialist through the analysis of histological images. However, these analyses are complex and the same case can lead to different understandings among pathologists, due to the exhaustive analysis of decisions, the time required and the presence of complex histological features. In this context, computational algorithms can be applied as tools to aid specialists through the application of segmentation methods to identify regions of interest that are essential for lymphomas diagnosis. In this paper, an unsupervised method for segmentation of nuclear components of neoplastic cells is proposed to analyze histological images of lymphoma stained with hematoxylin-eosin. The proposed method is based on the association among histogram equalization, Gaussian filter, fuzzy 3-partition entropy, genetic algorithm, morphological techniques and the valley-emphasis method in order to analyze neoplastic nuclear components, improve the contrast and illumination conditions, remove noise, split overlapping cells and refine contours. The results were evaluated through comparisons with those provided by a specialist and techniques available in the literature considering the metrics of accuracy, sensitivity, specificity and variation of information. The mean value of accuracy for the proposed method was 81.48%. Although the method obtained sensitivity rates between 41% and 51%, the accuracy values showed relevance when compared to those provided by other studies. Therefore, the novelties presented here may already encourage new studies with a more comprehensive overview of lymphoma segmentation.

What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review

“…In [49,65,89,[98][99][100][101][102][103][104][105][106][107][108][109][110][111][112], L * a * b * color space is used on LHIA for L * a * b * allowing color changes to be compatible with differences in visual perception. In [49,110,113,114], L * u * v * color space is used on LHIA for L * u * v * color space are uniform in perception.…”

“…In [49,65,89,[98][99][100][101][102][103][104][105][106][107][108][109][110][111][112], L * a * b * color space is used on LHIA for L * a * b * allowing color changes to be compatible with differences in visual perception. In [49,110,113,114], L * u * v * color space is used on LHIA for L * u * v * color space are uniform in perception. In [49,105,107,110,111,[115][116][117][118][119][120][121][122][123][124][125], HSV color space is used on LHIA for HSV separating the intensity from the color information.…”

“…In [49,110,113,114], L * u * v * color space is used on LHIA for L * u * v * color space are uniform in perception. In [49,105,107,110,111,[115][116][117][118][119][120][121][122][123][124][125], HSV color space is used on LHIA for HSV separating the intensity from the color information. In [103], HSI color space is used on LHIA for HSI is hue based color space similar to HSV.…”

“…In [103], HSI color space is used on LHIA for HSI is hue based color space similar to HSV. In [49,110], Ycbcr color space is used on LHIA. In [110], YIQ color space is used on LHIA.…”

Conflict management in the fusion of complementary segmentations of deformed kidneys and nephroblastoma

“…This 3D representation is currently done through manual segmentations, which is a time-consuming task. The French-Swiss border project SAIAD (Au- (Kato et al (2012)), and also the Fully Connected Conditional Random Fields (CRFs) Krähenbühl & Koltun (2011) coupled with CNNs to refine segmentations Chen et al (2018); Noh et al (2015); Kamnitsas et al (2017), genetic algorithm (Tosta et al (2017); Khan & Jaffar (2015)), and Case Based Reasoning (CBR) (Marie et al (2018); Kausar et al (2016); Frucci et al (2008)). But a single method is not efficient enough to achieve a correct segmentation of all structures on an image.…”