Abstract
Panoramic dental radiography is one of the most used images of the different dental specialties. This radiography provides information about the anatomical structures of the teeth. The correct evaluation of these radiographs is associated with a good quality of the image obtained. In this study, 598 patients were consecutively selected to undergo dental panoramic radiography at the Department of Radiology of the Faculty of Dentistry, Universidad Nacional de Asunción. Contrast enhancement techniques are used to enhance the visual quality of panoramic dental radiographs. Specifically, this article presents a new algorithm for contrast, detail and edge enhancement of panoramic dental radiographs. The proposed algorithm is called Multi-Scale Top-Hat transform powered by Geodesic Reconstruction for panoramic dental radiography enhancement (MSTHGR). This algorithm is based on multi-scale mathematical morphology techniques. The proposal extracts multiple features of brightness and darkness, through the reconstruction of the marker (obtained by the Top-Hat transformation by reconstruction) starting from the mask (obtained by the classic Top-Hat transformation). The maximum characteristics of brightness and darkness are added to the dental panoramic radiography. In this way, the contrast, details and edges of the panoramic radiographs of teeth are improved. For the tests, MSTHGR was compared with the following algorithms: Geodesic Reconstruction Multiscale Morphology Contrast Enhancement (GRMMCE), Histogram Equalization (HE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dual Sub-Image Histogram Equalization (DSIHE), Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE), Quadri-Histogram Equalization with Limited Contrast (QHELC), Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Gamma Correction (GC). Experimentally, the numerical results show that the MSTHGR obtained the best results with respect to the Contrast Improvement Ratio (CIR), Entropy (E) and Spatial Frequency (SF) metrics. This indicates that the algorithm performs better local enhancements on panoramic radiographs, improving their details and edges.
Highlights
Panoramic dental radiography is one of the most widely used medical imaging techniques, both by the general practitioner and in the various dental specialties [1,2,3].A good quality of the image obtained is essential to its correct interpretation
MSTHGR was compared with the following algorithms: Geodesic Reconstruction Multiscale Morphology Contrast Enhancement (GRMMCE) [27], Histogram Equalization (HE), Brightness Preserving Bi-Histogram Equalization (BBHE) [8], Dual Sub-Image Histogram
Contrast and detail enhancement of panoramic radiographs helps dentists better evaluate the anatomical structures of the teeth present in the image
Summary
A good quality of the image obtained is essential to its correct interpretation. This can be affected by various factors such as the device used, the acquisition technique and the subsequent processing of the acquired images [4,5]. One of the limitations of this type of images is that it is a two-dimensional representation of a three-dimensional object, so the different anatomical structures are superposed in the images obtained. Contrast enhancement techniques help the dentist make a better assessment of the radiographs, but they are used as a preprocessing of other more advanced automatic identification schemes based on deep learning [6]. Brightness, detail and edge enhancement algorithms have been proposed. Histogram-based algorithms are very popular for improving image contrast, brightness and detail. Among the most popular are Histogram Equalization (HE) and Contrast-Limited Adaptive Histogram
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