Hybrid Segmentation Technique Using Wavelet Packet and Watershed Transform for Medical Images

Abstract

Image segmentation is necessary but significant element in less intensity image investigation, pattern recognition, and in robotic systems. It is one of the most complex and demanding tasks in image processing. Image segmentation is the process of separating an image into various regions such that each region is identical. This paper proposes a new medical image segmentation method that integrates multi-resolution wavelet packet decomposition with the watershed transform for MRI image. The wavelet packet transform (WPT) is applied to the input image, creating detail and approximation coefficients. If watershed technique alone is used for segmentation, then over cluster is present. To overcome this, the proposed technique which combines wavelet packet and watershed algorithm is developed. First, the wavelet packet transform is applied to produce multi-resolution images, followed by applying watershed for segmentation to the approximation sub-bands. Finally, Inverse WPT is implemented to obtain the segmented image. Due to wavelet packet decomposition, the quantity of the disturbance can be decreased and leads to a tough segmentation. This proposed work concludes that wavelet packet and watershed transform facilitate to get the elevated precision even in strident images.