Morphology Based Non Uniform Background Removal for Particle Analysis: a Comparative Study

Non Uniform Illumination in an image often leads to diminished structures and inhomogeneous intensities due to different texture of the object surface and shadows cast from different light source directions. It is one of the most important factors affecting the appearance of an image, more adverse in case of biological images. Various techniques such as segmentation, edge detection and contrast enhancement using Histogram Equalization could not differentiate between some of the particles and their background. For applications, including particles/objects to be studied or analysed, these techniques give faulty results due to changes in actual shapes and sizes of the particles in the resulting image. This paper is aimed to remove these problems in microscopic image processing by removing the problem of non-uniform background illumination from the image using Morphological Opening, Adaptive Histogram Equalization and Edge detection techniques for particle analysis , a comparative study have been shown and a new algorithm is proposed for removing the problem of nonuniform background illumination in biological images such as visualizing and estimation of growth of a fungus in a particular sample to transform the input image to its indexed form with maximum accuracy involving morphological openings and structuring element design using Morphological Opening. The methodology used for solving the problem is estimating accurate background approximation as a http://www.ijccr.com VOLUME 1 ISSUE 3 MANUSCRIPT 14 NOVEMBER 2011 surface to extract the non-uniform background from the image and then constructing the new image by subtracting this estimated background from the original image. Results reveal the various particles present in the image with exact boundaries along with the removal of non-uniform illumination at the background. Histograms are plotted of the resulting images and compared at the end. Surface approximation plots for background of images from two techniques are compared to estimate the exact non-uniform background from image by both the steps.