Comparative Analysis of Hand-Crafted and Machine-Driven Histopathological Features for Prostate Cancer Classification and Segmentation
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| Format: | Preprint |
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2025
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| _version_ | 1866912198094225408 |
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| author | Baqain, Feda Bolus Al Al-Kadi, Omar Sultan |
| author_facet | Baqain, Feda Bolus Al Al-Kadi, Omar Sultan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_12415 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparative Analysis of Hand-Crafted and Machine-Driven Histopathological Features for Prostate Cancer Classification and Segmentation Baqain, Feda Bolus Al Al-Kadi, Omar Sultan Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Quantitative Methods 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. |
| title | Comparative Analysis of Hand-Crafted and Machine-Driven Histopathological Features for Prostate Cancer Classification and Segmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2501.12415 |