MULTIRESOLUTION ANALYSIS USING WAVELET TRANSFORMS FOR MEDICAL IMAGE SEGMENTATION

Abstract

The experimental study presented in this paper is aimed at the development of an automatic image segmentation system for classifying region of interest (ROI) in medical images which are obtained from medical scanners like MRI. Multiresolution analysis (MRA) using wavelet transforms has been used in the proposed segmentation system. It is particularly a challenging task to classify cancers in human organs in scanners output using shape or gray-level information; organs shape changes throw different slices in medical stack and the gray-level intensity overlap in soft tissues. This paper presents the application of wavelet transform to perform these task using the three dimensional waveletdecomposition, coefficients thresholding and object reconstruction. The proposed method is verified for simulated data at first and then applied for processing of brain parts to emphasize its selected components. The goal of the paper is in (i) the presentation of the three-dimensional wavelet transform, (ii) discussion of its use for volume data denoising, and (iii) proposal of the following data extraction to allow their classification. The paper compares numerical results achieved by the use of different wavelet functions and thresholding methods with the experience of an expert to propose the best algorithmic approach to this problem.