Log-Base2 of Gaussian kernel for nuclei segmentation from colorectal cancer H and E-stained histopathology images

Santanu Roy; Rahul Khurana; Vikas Jain

doi:10.1117/12.2679250

7 June 2023 Log-Base2 of Gaussian kernel for nuclei segmentation from colorectal cancer H and E-stained histopathology images

Santanu Roy, Rahul Khurana, Vikas Jain

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Proceedings Volume 12701, Fifteenth International Conference on Machine Vision (ICMV 2022); 127010B (2023) https://doi.org/10.1117/12.2679250
Event: Fifteenth International Conference on Machine Vision (ICMV 2022), 2022, Rome, Italy

Abstract

Nuclei Segmentation is a very essential and intermediate step for automatic cancer detection from H and E stained histopathology images. In the recent advent, the rise of Convolutional Neural Network (CNN), has enabled researchers to detect nuclei automatically from histopathology images with higher accuracy. However, the performance of automatic nuclei segmentation by CNN is fraught with overfitting, due to very less number of annotated segmented images available. Indeed, we find that the problem of nuclei segmentation is an unsupervised problem, because still now there is no automatic tool available which can make annotated images (nuclei segmented images) accurately, to the best of our knowledge. In this research article, we present a Logarithmic-Base2 of Gaussian (Log-Base2-G) Kernel which has the ability to track only the nuclei portions automatically from Colorectal Cancer H and E stained histopathology images. First, Log-Base2-G Kernel is applied to the input images. Thereafter, we apply an adaptive Canny Edge detector, in order to segment only the nuclei edges from H and E stained histopathology images. Experimental results revealed that our proposed method achieved higher accuracy and F1 score, without the help of any annotated data which is a significant improvement. We have used two different datasets (Con-SeP dataset, and Glass-contest dataset, both contains Colorectal Cancer histopathology images) to check the effectiveness and validity of our proposed method. These results have shown that our proposed method outperformed other image processing or unsupervised methods both qualitatively and quantitatively.

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Santanu Roy, Rahul Khurana, and Vikas Jain "Log-Base2 of Gaussian kernel for nuclei segmentation from colorectal cancer H and E-stained histopathology images", Proc. SPIE 12701, Fifteenth International Conference on Machine Vision (ICMV 2022), 127010B (7 June 2023); https://doi.org/10.1117/12.2679250

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KEYWORDS

Image segmentation

Image processing

Histopathology

Edge detection

Gaussian filters

Tunable filters

Colorectal cancer

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