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.
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