Caries detection enhancement using texture feature maps of intraoral radiographs

Abstract

Objectives Dental caries are caused by tooth demineralization due to bacterial plaque formation. However, the resulting lesions are often discrete and thus barely recognizable in intraoral radiography images. Therefore, more advanced detection techniques are in great demand among dentists and radiographers. This study was performed to evaluate the performance of texture feature maps in the recognition of discrete demineralization related to caries plaque formation. Methods Digital intraoral radiology image analysis protocols incorporating first-order features (FOF), co-occurrence matrices, gray tone difference matrices, run-length matrices (RLM), local binary patterns (LBP), and k-means clustering (CLU) were used to transform the digital intraoral radiology images of 10 patients with confirmed caries, which were retrospectively reviewed in a dental clinic. The performance of the resulting texture feature maps was compared with that of radiographic images by radiologists and dental specialists. Results Significantly improved detection of caries spots was achieved by employing the CLU and FOF texture feature maps. The caries-affected area with sharp margins was well defined using the CLU approach. A pseudo-three-dimensional effect was observed in outlining the demineralization zones inside the cavity with the FOF 5 protocol. In contrast, the LBP and RLM techniques produced less satisfactory results with unsharp edges and less detailed depiction of the lesions. Conclusions This study illustrated the applicability of texture feature maps to the recognition of demineralized spots on the tooth surface debilitated by caries and identified the best performing techniques.

Use of Texture Feature Maps for the Refinement of Information Derived from Digital Intraoral Radiographs of Lytic and Sclerotic Lesions

“…The co-occurrence matrices (COM) [21], first-order features (FOF), gray-tone difference matrices (GTDM) [22], run-length matrices (RLM) [23], and local binary patterns (LBP) [24,25] were applied. The details of the texture methods are described in [26]. Most of the mentioned methods (COM, FOF, GTDM, and RLM) in the original version compute several features to describe the whole image content.…”

Descriptive analysis of dental X-ray images using various practical methods: A review

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Analysis of X-rays in bone remodelling around Active and Replace dental implants

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