Log-Base2 of Gaussian Kernel for Nuclei Segmentation from Colorectal Cancer H and E-Stained Histopathology Images

Title

Log-Base2 of Gaussian Kernel for Nuclei Segmentation from Colorectal Cancer H and E-Stained Histopathology Images

Creator

Roy S.; Khurana R.; Jain V.

Description

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. 2023 SPIE.

Source

Proceedings of SPIE - The International Society for Optical Engineering, Vol-12701

Date

2023-01-01

Publisher

SPIE

Subject

Canny edge detection; Gaussian filter; Histopathology Images; Laplacian of Gaussian (LoG); Logarithm Base-2 of Gaussian (Log-Base2-G) Kernel; Nuclei Segmentation

Coverage

Roy S., School of Engineering and Technology, Christ (Deemed to be University), Bangalore, India; Khurana R., Department of Computer Science and Engineering, Bharati Vidyapeeths College of Engineering, New Delhi, India; Jain V., School of Computer Science, Engineering and Technology, Bennett University, Greater Noida, India

Rights

Restricted Access

Relation

ISSN: 0277786X; ISBN: 978-151066618-4; CODEN: PSISD

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1117/12.2679250

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172915740&doi=10.1117%2f12.2679250&partnerID=40&md5=fdaaf59ef50507eb4cfe77cfb8821833