Enhancing textural differences using wavelet-based texture characteristics morphological component analysis: A preprocessing method for improving image segmentation

摘要

In a recent paper, a method called “texture characteristic based morphological component analysis” (TC-MCA) has been proposed to enhance the performances of texture-based image segmentation algorithms: (1) TC-MCA separates texture into multiple pairs of components each representing a different visual characteristic of the texture; (2) then the TC-MCA algorithm manipulates each component and recombines them to produce the enhanced image where textures are more different, so that the texture-based segmentation is improved. This paper proposes a novel method outperforming TC-MCA by: (1) applying dictionaries based on dual-tree complex wavelet transform (DTCWT) to decompose the image into components representing different visual characteristics of texture, and (2) manipulating the dual-tree complex wavelet coefficients of the texture components to enhance each texture component’s own property and producing an texture-difference-enhanced image by recombining the enhanced components. The wavelet-based texture characteristic morphological component analysis (WT-TC-MCA) is applied as the preprocessing step to some state-of-the-art texture-based segmentation algorithms and compared with other texture enhancing methods, including the TC-MCA, with respect to the accuracy, precision-recall curves of the segmentation results. Experiments demonstrate that our method produces better textural difference enhancement effects and improves texture-based segmentation more than the comparators.