Segmentation of microscope cell images via adaptive eigenfilters

This paper presents the use of a PCA based approach to segment cells from RGB light microscope images. The proposed segmentation is accurate and robust under uneven illumination, lighting variation, and noise. Principal component analysis (PCA) is first applied to the RGB color bands of the image. The image corresponding to the principal component has significantly better contrast over the original image. A set of eigenfilters is then obtained by applying PCA to local neighborhoods of this image. A pair of filters from this set, corresponding to the second and third largest eigenvalues, resembles ramp edge filters with orientations that adapt to the image. These edge filters are used to obtain the edgemap of the image. We define a criterion that enables accurate detection of valid edges of cells while suppressing noise.