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Automatic computer-aided caries detection from dental x-ray images using intelligent level set

Rad, AE, Rahim, MSM, Kolivand, H and Norouzi, A (2018) Automatic computer-aided caries detection from dental x-ray images using intelligent level set. Multimedia Tools and Applications. ISSN 1380-7501

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Abstract

Dental diseases have high risk of affection across the globe and mostly in adult population. The analysis of dental X-ray images has some difficulties in comparison to other medical images, which makes segmentation a more challenging process. One of the most important and yet largely unsolved issues in the level set method framework is the efficiency of signed force, speed function and initial contour (IC) generation. In this paper, a new segmentation method based on level set (LS) is proposed in two phases; IC generation using morphological information of image and intelligent level set segmentation utilizing motion filtering and back propagation neural network. The segmentation results are efficient and accurate as compared to other studies. The new approach to isolate each segmented teeth image is proposed by employing integral projection technique and feature map designed for each tooth to extract the local information and therefore to detect caries area. The achieved overall performance of the proposed segmentation method was evaluated at 120 periapical dental radiograph (X-ray), with images at 90% and the detection accuracy of 98%.

Item Type: Article
Additional Information: This is a post-peer-review, pre-copyedit version of an article published in Multimedia Tools and Applications. The final authenticated version is available online at: http://dx.doi.org/10.1007/s11042-018-6035-0
Uncontrolled Keywords: 0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems, 0801 Artificial Intelligence And Image Processing
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science
Publisher: Springer Verlag (Germany)
Date Deposited: 16 May 2018 09:35
Last Modified: 16 May 2018 09:35
DOI or Identification number: 10.1007/s11042-018-6035-0
URI: http://researchonline.ljmu.ac.uk/id/eprint/8675

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