Computer Aided Detection of Oral Lesions on CT Images
Oral lesions are important findings on computed tomography (CT) images. In this study, a fully automatic method to detect oral lesions in mandibular region from dental CT images is proposed. Two methods were developed to recognize two types of lesions namely (1) Close border (CB) lesions and (2) Open border (OB) lesions, which cover most of the lesion types that can be found on CT images. For the detection of CB lesions, fifteen features were extracted from each initial lesion candidates and multi layer perceptron (MLP) neural network was used to classify suspicious regions. Moreover, OB lesions were detected using a rule based image processing method, where no feature extraction or classification algorithm were used. The results were validated using a CT dataset of 52 patients, where 22 patients had abnormalities and 30 patients were normal. Using non-training dataset, CB detection algorithm yielded 71% sensitivity with 0.31 false positives per patient. Furthermore, OB detection algorithm achieved 100% sensitivity with 0.13 false positives per patient. Results suggest that, the proposed framework, which consists of two methods, has the potential to be used in clinical context, and assist radiologists for better diagnosis.
S. Galib et al., "Computer Aided Detection of Oral Lesions on CT Images," Journal of Instrumentation, vol. 10, no. 12, Institute of Physics - IOP Publishing, Dec 2015.
The definitive version is available at https://doi.org/10.1088/1748-0221/10/12/C12030
International Workshop on Imaging (2015: Sep. 7-10, Varenna, Italy)
Nuclear Engineering and Radiation Science
Keywords and Phrases
Algorithms; Computer aided diagnosis; Diagnosis; Feature extraction; Image processing; Image reconstruction; Medical image processing; Medical imaging; Signal detection; Classification algorithm; Computed radiography (CR); Computer aided; Computer aided detection; Detection algorithm; Image processing - methods; Multi layer perceptron; Training dataset; Computerized tomography (CT); Medical-image reconstruction methods and algorithms; Computer-aided diagnosis; Computer-aided software
International Standard Serial Number (ISSN)
Article - Conference proceedings
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01 Dec 2015