Computer Aided Detection of Oral Lesions on CT Images
This work addresses the need for computer-aided detection to assist radiologists in diagnosing oral lesions from CT images, but it is incremental as it applies existing methods like MLP and rule-based processing to a specific medical domain.
The study tackled the problem of automatically detecting oral lesions on CT images by developing two methods for close border and open border lesions, achieving 71% sensitivity with 0.31 false positives per patient for CB lesions and 100% sensitivity with 0.13 false positives per patient for OB lesions.
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.