Image Segmentation using Multiresolution Texture Gradient and Watershed Algorithm

Abstract

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.

A review on the wavelet methods for sonar image segmentation

“…The good space-frequency local characteristics of the wavelet transform are very suitable for image processing. [50][51][52][53][54][55][56][57][58] Research status of sonar image region (or texture) segmentation via the wavelet transform A given image can be analyzed at various resolution levels via the wavelet transform. For a sonar image, since different textures are recorded with different resolutions, therefore the wavelet coefficients in different sub-bands work efficiently for texture analysis and classification.…”

“…Due to some advantages over the Fourier transform, the wavelet and wavelet packet transforms (decompositions) are widely used in various fields of image processing. [50][51][52][53][54][55][56][57][58]93 The wavelet and wavelet packet bases are not the best tools for image representation because they can only express the position and characteristics of the singular points and cannot fully characterize geometrical features such as multidirectional edges and textures in images. 93 Do and Vetterli proposed that an excellent tool for image representation is supposed to meet the following natures: (1) multiresolution, (2) localization, (3) critical sampling, (4) directionality, and (5) anisotropy.…”

“…The methods are suitable for image segmentation due to their good spatial/frequency localization ability and multiscale (multiresolution) analysis ability. [50][51][52][53][54][55][56][57] Gonzalez and Woods also stated "The appeal of such an approach (wavelet-based method, the authors add the note) is obvious-features that might go undetected at one resolution may be easy to detect at another." 58 Hence, the waveletbased methods for sonar image segmentation are attractive.…”

A Modified Watershed Segmentation Method to Segment Renal Calculi in Ultrasound Kidney Images

“…The gradient magnitude of an image is considered as a topographic surface for the watershed transformation (Gupta, Gosain, & Kaushal 2010). The watershed transformation based segmentation approach function based on the morphological principle (Roshni & Raju, 2011). The watershed transformation technique is used for reducing the over-segmentation of the watershed algorithm (Salman, 2006).…”