Image Segmentation using Multiresolution Texture Gradient and Watershed Algorithm

The wavelet transform as an important multi resolution analysis tool has already been commonly applied to texture analysis and classification. Mathematical morphology is very attractive for automatic image segmentation because it efficiently deals with geometrical descriptions such as size, area, shape, or connectivity that can be considered as segmentation-oriented features. This paper presents an image-segmentation system based on some well-known strategies implemented in a different methodology. The segmentation process is divided into three basic steps, namely: texture gradient extraction, marker extraction, and boundary decision. Texture information and its gradient are extracted using the decimated form of a complex wavelet packet transform. A novel marker location algorithm is subsequently used to locate significant homogeneous textured or non textured regions. The goal of boundary decision is to precisely locate the boundary of regions detected by the marker extraction. This decision is based on a region-growing algorithm which is a modified flooding based watershed algorithm.