Computer Aided Detection of Oral Lesions on CT Images

TL;DR

This paper presents a fully automatic CT image analysis framework for detecting oral lesions, combining neural network classification for close border lesions and rule-based detection for open border lesions, validated on patient data.

Contribution

It introduces a novel dual-method approach for oral lesion detection on CT images, integrating neural network and rule-based techniques for improved accuracy.

Findings

CB detection achieved 71% sensitivity with 0.31 false positives per patient

OB detection achieved 100% sensitivity with 0.13 false positives per patient

Framework shows potential for clinical application in aiding diagnosis

Abstract

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.