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Segmentation of Parotid Lesions in CT Images using Wavelet-based Features

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IJCA Special Issue on Recent Trends in Pattern Recognition and Image Analysis
© 2013 by IJCA Journal
RTPRIA
Year of Publication: 2013
Authors:
Tung-ying Wu
Sheng-fuu Lin
10.5120/11798-1004

Tung-ying Wu and Sheng-fuu Lin. Article: Segmentation of Parotid Lesions in CT Images using Wavelet-based Features. IJCA Special Issue on Recent Trends in Pattern Recognition and Image Analysis RTPRIA(1):18-26, May 2013. Full text available. BibTeX

@article{key:article,
	author = {Tung-ying Wu and Sheng-fuu Lin},
	title = {Article: Segmentation of Parotid Lesions in CT Images using Wavelet-based Features},
	journal = {IJCA Special Issue on Recent Trends in Pattern Recognition and Image Analysis},
	year = {2013},
	volume = {RTPRIA},
	number = {1},
	pages = {18-26},
	month = {May},
	note = {Full text available}
}

Abstract

Automatic segmentation of parotid glands for computer-aided diagnosis in clinical practice is still a challenging task, especially when there are lesions needing to be outlined. In the applications of image-based diagnosis and computer-aided lesion detection, image segmentation is an important procedure. Features extracted from image analysis in companion with image segmentation algorithms are used to provide region-based information for clinical evaluation procedures. In this paper, we describe a method for segmenting the parotid regions with skeptical lesions in the head and neck CT images. At first, à trous, a modified discrete wavelet transform algorithm, is introduced to decompose an image into sub-bands, and the feature descriptors effective for soft tissues characteristics are computed using the derived coefficients in the sub-bands. Then, clustering algorithms are proposed to connect the pixels corresponding to similar features into several regions of the soft tissues, and so do the tissues of the lesions. In this paper, a comparative study of feature-based segmentation with three methods is carried on, and the extracted regions are compared with the segmentation from the experts for evaluating the performance.

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