Comparative Analysis of Hand-Crafted and Machine-Driven Histopathological Features for Prostate Cancer Classification and Segmentation

TL;DR

This paper compares hand-crafted and machine-driven features for prostate cancer image segmentation, showing that U-Net-based machine features outperform traditional methods in segmentation quality and accuracy.

Contribution

It provides a comprehensive comparison between traditional texture descriptors and deep learning features for prostate tissue segmentation and classification.

Findings

U-Net achieves 94% classification accuracy.

Machine-driven features outperform hand-crafted features in segmentation quality.

Support vector machine with GLCM and LBP achieves over 95% accuracy.

Abstract

Histopathological image analysis is a reliable method for prostate cancer identification. In this paper, we present a comparative analysis of two approaches for segmenting glandular structures in prostate images to automate Gleason grading. The first approach utilizes a hand-crafted learning technique, combining Gray Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP) texture descriptors to highlight spatial dependencies and minimize information loss at the pixel level. For machine driven feature extraction, we employ a U-Net convolutional neural network to perform semantic segmentation of prostate gland stroma tissue. Support vector machine-based learning of hand-crafted features achieves impressive classification accuracies of 99.0% and 95.1% for GLCM and LBP, respectively, while the U-Net-based machine-driven features attain 94% accuracy. Furthermore, a comparative analysis demonstrates superior segmentation quality for histopathological grades 1, 2, 3, and 4 using the U-Net approach, as assessed by Jaccard and Dice metrics. This work underscores the utility of machine-driven features in clinical applications that rely on automated pixel-level segmentation in prostate tissue images.