A Segmentation Method For Cell Image Based On Two-Dimensional Histogram

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Abstract
A scene segmentation method is proposed for Papanicolaou stained gynecologic cervical cells. The method is based on the maximum likelihood classifier for two-dimensional pixels consisted of optical densities specified at points on a red image and a green one. The images were provided as two scanned monochromic images illuminated with lights of 610nm and 535nm in wavelengths. Distributions of the pixels form a two-dimensional histogram, and there are three clusters supposed to be about the pixels, namely, clusters for background's pixels, cytoplasmic pixels, and nuclear pixels. This paper describes a method to calculate unknown parameters, namely the mean vector, the covarience matrix and the constant, in an unnormalized density function for each cluster of the pixels from the two-dimensional histogram by assuming the distribution to be normal.
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