In digital image processing and computer vision, segmentation operation for an image refers to dividing an image into multiple image segments, and the significant purpose of segmentation operation is to depict an image in a way so that the analysis process of the objects of interest is easier and more accurate. The region-based segmentation scheme act for finding similarities between adjacent pixels to detect each region that constructs the image. Similarity scales have based on different features, in a grayscale image, the scale may be referred to as textures and other spatial appearances, and also the variance in intensity of a region and so on. Significantly, many applications in different fields involved region-based segmentation for instance remote sensing, medical application, and others for recognizing interesting objects in an image. In this paper, two techniques for segmentation operation in region-based which are region growing and watershed are reviewed.

To make the image easily represented for more analysis and processing the segmentation procedure is required, where the image is portioned into its formed regions using some segmentation techniques based on features extraction. In this paper, a proposed procedure for finding the regions that formed the image is achieved based on hybrid features in two different components of different two colors spaces L*a*b* and RGB segmented by the k-means method. The hybrid features which comprise the mask segmentation are a combination of texture image characterization extracted by the Gabor filter and gradient image intensity by the Sobel operator after image quality enhancement by applying wiener filter noise reduction and contrast enhancement using Contrast limited adaptive equalization (CLAHE). Some statistical metrics are used for evaluating the performance of the proposed work stages.