Wavelet Energy Guided Level Set for Segmenting Highly Similar Regions in Medical Images

This paper presents a new segmentation method that integrates a wavelet based feature, which is able to enhance the dissimilarity between regions with low variations in intensity. This feature is integrated to formulate a new level set based active contour model that addresses the segmentation of regions with highly similar intensities in medical images, which do not have clear boundaries between them. In the first phase of this research, the strength of wavelet transform is adapted to formulate a statistical feature, named as wavelet energy. The second phase of this work is dedicated to formulate a new level set based active contour model that is suitable for segmenting regions without clear boundaries and exhibits intensity inhomogeneity within the region. The proposed segmentation method is named wavelet energy guided level set based active contour. Experiments are conducted using medical images focusing on the neck and bladder regions to validate the proposed method. The experimental results show that the proposed method is able to segment the region of interest in close correspondence with the manual delineation by the radiologists.