Digital imaging of colon tissue: method for evaluation of inflammation severity by spatial frequency features of the histological images

Abstract

Background The efficacy of histological analysis of colon sections used for evaluation of inflammation severity can be improved by means of digital imaging giving quantitative estimates of main diagnostic features. The aim of this study was to reveal most valuable diagnostic features reflecting inflammation severity in colon and elaborate the evaluation method for computer-aided diagnostics. Methods Tissue specimens from 24 BALB/c mice and 15 patients were included in the study. Chronic and acute colon inflammation in mice was induced by oral administration of dextran sulphate sodium (DSS) solution, while mice in the control group did not get DSS. Human samples of inflamed colon tissue were obtained from patients with ulcerative colitis ( n = 6). Non-inflamed colon tissue of control subjects ( n = 9) was obtained from patients with irritable bowel syndrome or functional obstipation. Analysis of morphological changes in mice and human colon mucosa was performed using 4-μm haematoxylin-eosin (HE) sections. The features reflecting morphological changes in the images of colon mucosa were calculated by convolution of Gabor filter bank and array of pixel values. All features were generalized by calculating mean, histogram skewness and entropy of every image response. Principal component analysis was used to construct optimal representation of morphological changes. Results First principal component (PC1) was representing the major part of features variation (97 % in mice and 71 % in human specimens) and was selected as a measure of inflammation severity. Validation of new measure was performed by means of custom-made software realizing double blind comparison of differences in PC1 with expert’s opinion about inflammation severity presented in two compared pictures. Overall accuracy of 80 % for mice and 67 % for human was reached. Conclusion Principal component analysis of spatial frequency features of histological images may provide continuous scale estimation of inflammation severity of colon tissue.

Molecular profiling of the intestinal mucosa and immune cells of the colon by multi-parametric histological techniques

“…Despite significant progress in digital pathology that has been made for decades, histopathological diagnosis continues to be a major clinical challenge and requires an interdisciplinary approach to be optimal 13 . The assessment of spatial frequency data of histological images allows the quantification of inflammatory parameters and improves evaluation of inflammation in the colon, which in relation to digital pathology can contribute to a more accurate diagnosis and can more accurately differentiate the immune cells 47 .…”

Histopathology of thymectomy specimens from the MGTX-trial: Entropy analysis as strategy to quantify spatial heterogeneity of lymphoid follicle and fat distribution

“…range of the follicle number or standard deviation of fat content) can describe the variance of one feature, but is not suitable to measure the compound spatial heterogeneity of variables with different measurement scales, we here propose a probability based model for the quantification of heterogeneity based on entropy measurement (in bit) per case [ 10 ]. This strategy has been successfully used in other scientific disciplines such as ecology [ 10 ] and digital image processing [ 11 – 14 ]. In these fields, entropy was defined in terms of information theory [ 15 , 16 ] and has been used to quantify spatial heterogeneity.…”