Adaptive image quantization for discrimination of cervical pre-cancer

Gyana Ranjan Sahoo; Jaidip Jagtap; P. K. Panigrahi; Asima Pradhan; Madhur Srivastava

doi:10.1117/12.3021982

20 June 2024 Adaptive image quantization for discrimination of cervical pre-cancer

Gyana Ranjan Sahoo, Jaidip Jagtap, P. K. Panigrahi, Asima Pradhan, Madhur Srivastava

Proceedings Volume 13006, Biomedical Spectroscopy, Microscopy, and Imaging III; 1300612 (2024) https://doi.org/10.1117/12.3021982
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

We demonstrate a Bayesian statistics-based outlier separation algorithm, which clearly distinguishes microscope captured images of unstained human cervical tissue sections of normal and different grades of precancerous tissues. The semi-automated global and adaptive method implements outlier separation based on the statistical characterization of the image histogram distribution. This multi-level thresholding achieves an effective image quantization of the high cell density domain, most affected in the progression of the disease, which yields a precise visualization of the lesions in the epithelium cellular structures, revealing their temporal changes with the progression of the disease. The pixel count ratio of the quantized high cell density region, below a statistically well-defined threshold, quantitatively discriminates different grades of precancer tissues through Receiver Operating Characteristics.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Gyana Ranjan Sahoo, Jaidip Jagtap, P. K. Panigrahi, Asima Pradhan, and Madhur Srivastava "Adaptive image quantization for discrimination of cervical pre-cancer", Proc. SPIE 13006, Biomedical Spectroscopy, Microscopy, and Imaging III, 1300612 (20 June 2024); https://doi.org/10.1117/12.3021982

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