Detecting algorithm based Gabor in microscopic image

It is very important to identify the pathology components of urine solution and acquire a result of clinical examination. It aids to diagnose diseases of the urology system. Because the particles such as urinary sediments are irregular in shape and fuzzy in edge, it is much more difficult to automatically detect particles in microscopic images. Texture segmentation involves subdividing an image into differently textured regions. Many segmentation based on Gabor filters are derived from Gabor elementary functions. We present a new robust method for the problem of automatically detecting the particles' location in images. We have devised a more rigorously Gabor-based method enhancing the edge information for designing Gabor filters. We argue that Gabor filter outputs can be modeled as Gaussian's and develop a new algorithm for selecting optimal filter parameters.