Semi-automatic computer aided lesion detection in dental X-rays using variational level set

  • Authors:

  • Shuo Li;Thomas Fevens;Adam Krzyak;Chao Jin;Song Li

  • Affiliations:

  • GE Healthcare, London, Canada;Medical Imaging Group, Department of Software Engineering and Computer Science, Concordia University, Montréal, Que., Canada;Medical Imaging Group, Department of Software Engineering and Computer Science, Concordia University, Montréal, Que., Canada;Medical Imaging Group, Department of Software Engineering and Computer Science, Concordia University, Montréal, Que., Canada;School of Stomatology, Anhui Medical University, Hefei, Anhui, PR China

  • Venue:
  • Pattern Recognition
  • Year:
  • 2007

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Abstract

A semi-automatic lesion detection framework is proposed to detect areas of lesions from periapical dental X-rays using level set method. In this framework, first, a new proposed competitive coupled level set method is used to segment the image into three pathologically meaningful regions using two coupled level set functions. Tailored for the dental clinical setting, a two-stage clinical segmentation acceleration scheme is used. The method uses a trained support vector machine (SVM) classifier to provide an initial contour for two coupled level sets. Then, based on the segmentation results, an analysis scheme is applied. Firstly, the scheme builds an uncertainty map from which those areas with radiolucent will be automatically emphasized by a proposed color emphasis scheme. Those radiolucent in the teeth or jaw usually suggested possible lesions. Secondly, the scheme employs a method based on the average intensity profile to isolate the teeth and locate two types of lesions: periapical lesion (PL) and bifurcation lesion (BL). Experimental results show that our proposed segmentation method is able to segment the image into pathological meaningful regions for further analysis; our proposed framework is able to automatically provide direct visual cues for the lesion detection; and when given the orientation of the teeth, it is able to automatically locate the PL and BL with a seriousness level marked for further dental diagnosis. When used in the clinical setting, the framework enables dentist to improve interpretation and to focus their attention on critical areas.