  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.
