Moore Pseudo Inverse Histology Analysis and Fully Convoluted Watershed Segmentation for Cancer Grading
Moore Pseudo Inverse Histology Analysis and Fully Convoluted Watershed Segmentation for Cancer Grading |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-5 |
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| Year of Publication : 2025 | ||
| Author : Peermohamed. A, Sulthan Ibrahim. M |
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| DOI : 10.14445/22315381/IJETT-V73I5P116 | ||
How to Cite?
Peermohamed. A, Sulthan Ibrahim. M, "Moore Pseudo Inverse Histology Analysis and Fully Convoluted Watershed Segmentation for Cancer Grading," International Journal of Engineering Trends and Technology, vol. 73, no. 5, pp.173-185, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I5P116
Abstract
The National Cancer Institute defines histopathological images as the study of diseased cells using a microscope. The pathologist investigates the tissue structure, cell tissue distribution, and cell shape regularities and decides on benign and malignancy in the image. However, the process is found to be more laborious, time-consuming and highly prone to intra and inter-observer variability. To deal with this gap, in this work, a method called, Moore Penrose Pseudo Inverse and Fully Convolution-based Watershed Segmentation (MPPI-FCWS) is proposed. The MPPI-FCWS method is split into two parts, namely preprocessing and segmentation. Initially, the raw histology images obtained from breast histopathology images are subjected to preprocessing using the Moore–Penrose pseudoinverse matrix. Here, normalization and denoising are performed with the objective of identifying metastatic tissue in histopathologic scans of lymph node sections. Second, the process By focusing on the artifacts, the error rate involved in analysis can be reduced. Next, the segmentation of tissues is performed using Fully Convolution-based Watershed Segmentation that focuses on the separation of the region of interest from background tissues as well as the separation of nuclei from cytoplasm, therefore minimizing segmentation error significantly. Experimental evaluation of the proposed MPPI-FCWS method and existing methods are carried out with respect to the number of sample images. The proposed method carries out the experimental evaluation using factors such as precision, recall, accuracy and error rate. The proposed MPPI-FCWS method improved precision and recall by 9% and 31% with a high accuracy rate of 18%.
Keywords
Histopathological, Moore penrose, Normalization, Pseudo inverse, Fully convolution, Watershed segmentation.
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