Fuzzy Image Segmentation Algorithms in Wavelet Domain

Abstract

The images are considered one of the most important means of information transmission; therefore the image processing has become an important tool in a variety of fields such as video coding, computer vision and medical imaging. Within the image processing, there is the segmentation process that involves partitioning an image into a set of homogeneous and meaningful regions, such that the pixels in each partitioned region possess an identical set of properties or attributes (Gonzalez & Woods, 1992). The sets of properties of the image may include gray levels, contrast, spectral values, or texture properties, etc. The result of segmentation is a number of homogeneous regions, each having a unique label. Image segmentation is often considered to be the most important task in computer vision. However, the segmentation in images is a challenging task due to several reasons: irregular and dispersive lesion borders, low contrast, artifacts in the image and variety of colors within the interest region. Therefore, numerous methods have been developed for image segmentation within applications in the computer vision. Image segmentation can be classified into three categories: A) Supervised.These methods require the interactivity in which the pixels belonging to the same intensity range pointed out manually and segmented. B) Automatic.This is also known as unsupervised methods, where the algorithms need some priori information, so these methods are more complex, and C) Semi-automatic.That is the combination of manual and automatic segmentation. Some of practical applications of image segmentation are: the medical imaging tasks that consist of location of tumors and other pathologies, recognition of the objects in images of remote sensing obtained via satellite or aerial platforms, automated-recognition systems to inspect the electronic assemblies, biometrics, automatic traffic controlling systems, machine vision, separating and tracking the regions appearing in consequent frames of an sequence, and finally, the real time mobile robot applications employing vision systems. 1.