Integration of Marker Controlled Watershed and Region Merging Method for Image Segmentation

Abstract

Automatic image segmentation is a very important task for image analysis, object detection and recognition tasks. In this research, automatic image segmentation system is proposed which includes three main approaches: preprocessing, segmentation and post processing approach. The preprocessing step estimates a better approximation of gradient magnitudes by the modified 7x7 Laplacian of Gaussian (LoG) edge filter. In segmentation step, marker controlled watershed method (MCWS) is applied to solve oversegmentation problem. Finally, the segmented regions are merged by using histogram similarity to obtain the accurate segmented regions in an image. This system is tested on two different kinds of datasets: medical image dataset and color natural image dataset. In this research, this system has also achieved accuracy 93.01% for brain image, 76.72% for color natural image. The running time of the proposed system takes five times than MCWS method for medical images due to region merging process for many complex regions.