Computer aided root decay detection using level set and complex wavelets

Shuo Li; Thomas Fevens; Adam Krzyzak; Chao Jin; Song Li

doi:10.1117/12.709030

30 March 2007 Computer aided root lesion detection using level set and complex wavelets

Shuo Li, Thomas Fevens, Adam Krzyzak, Chao Jin, Song Li

Proceedings Volume 6514, Medical Imaging 2007: Computer-Aided Diagnosis; 65141M (2007) https://doi.org/10.1117/12.709030
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

A computer aided root lesion detection method for digital dental X-rays is proposed using level set and complex wavelets. The detection method consists of two stages: preprocessing and root lesion detection. During preprocessing, a level set segmentation is applied to separate the teeth from the background. Tailored for the dental clinical environment, a segmentation clinical acceleration scheme is applied by using a support vector machine (SVM) classifier and individual principal component analysis (PCA) to provide an initial contour. Then, based on the segmentation result, root lesion detection is performed. Firstly, the teeth are isolated by the average intensity profile. Secondly, a center-line zero crossing based candidate generation is applied to generate the possible root lesion areas. Thirdly, the Dual-Tree Complex Wavelets Transform (DT-CWT) is used to further remove false positives. Lastly when the root lesion is detected, the area of root lesion is automatically marked with color indication representing different levels of seriousness. 150 real dental X-rays with various degrees of root lesions are used to test the proposed method. The results were validated by the dentist. Experimental results show that the proposed method is able to successfully detect the root lesion and provide visual assistance to the dentist.

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Shuo Li, Thomas Fevens, Adam Krzyzak, Chao Jin, and Song Li "Computer aided root lesion detection using level set and complex wavelets", Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65141M (30 March 2007); https://doi.org/10.1117/12.709030

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KEYWORDS

Teeth

Image segmentation

X-rays

Feature extraction

Wavelets

Principal component analysis

Bone

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