Selection of Filtering and Image Texture Analysis in the Radiographic Images Processing of Horses’ Incisor Teeth Affected by the EOTRH Syndrome

Abstract

Equine odontoclastic tooth resorption and hypercementosis (EOTRH) is one of the horses’ dental diseases, mainly affecting the incisor teeth. An increase in the incidence of aged horses and a painful progressive course of the disease create the need for improved early diagnosis. Besides clinical findings, EOTRH recognition is based on the typical radiographic findings, including levels of dental resorption and hypercementosis. This study aimed to introduce digital processing methods to equine dental radiographic images and identify texture features changing with disease progression. The radiographs of maxillary incisor teeth from 80 horses were obtained. Each incisor was annotated by separate masks and clinically classified as 0, 1, 2, or 3 EOTRH degrees. Images were filtered by Mean, Median, Normalize, Bilateral, Binomial, CurvatureFlow, LaplacianSharpening, DiscreteGaussian, and SmoothingRecursiveGaussian filters independently, and 93 features of image texture were extracted using First Order Statistics (FOS), Gray Level Co-occurrence Matrix (GLCM), Neighbouring Gray Tone Difference Matrix (NGTDM), Gray Level Dependence Matrix (GLDM), Gray Level Run Length Matrix (GLRLM), and Gray Level Size Zone Matrix (GLSZM) approaches. The most informative processing was selected. GLCM and GLRLM return the most favorable features for the quantitative evaluation of radiographic signs of the EOTRH syndrome, which may be supported by filtering by filters improving the edge delimitation.

Application of Two-Dimensional Entropy Measures to Detect the Radiographic Signs of Tooth Resorption and Hypercementosis in an Equine Model

“…Radiographs were digitally processed in two steps including input image filtering ( Figure 1 D) and output image texture analysis ( Figure 1 E). For the image filtering, three filtering algorithms were chosen: Normalize, Median, and Laplacian Sharpening based on the previously described findings of equine incisor teeth radiographs evaluation [ 17 ]. For the image texture analysis, five entropy–based texture measures were considered: two–dimensional sample entropy (SampEn2D), two–dimensional fuzzy entropy (FuzzEn2D), two–dimensional permutation entropy (PermEn2D), two–dimensional dispersion entropy (DispEn2D), and two–dimensional distribution entropy (DistEn2D), based on the previously described applicability in the equine image evaluation [ 27 , 28 ].…”

“…Resorption and hypercementosis are two ongoing processes that progressively affect the structure of the tooth starting from the alveolar aspect (root and the reserve crown) [ 15 ]. Both processes are radiographically well–defined, as teeth demonstrate high radiodensity [ 16 ], where low radiopaque signs of resorption and high radiopaque signs of hypercementosis are alternately visible, thus, presenting the high irregularity and complexity of the image texture of radiographs [ 17 ].…”

“…It has been shown that raw radiographs collected directly from the X–ray scanner, which have not been digitally processed, are less effective in incisor teeth radiographic texture quantification than those that have been digitally processed. Due to the presence of noise, the radiographic filtering by filters improves the edge delimitation, such as Normalize or Bilateral filters, which increases the recognition of the radiographic signs of EOTRH syndrome [ 17 ]. However, the specific detection of grade 1, grade 2, and grade 3 EOTRH radiological signs remains challenging.…”

“…With the rapid advances in diagnostic imaging in equine dentistry [ 17 , 18 , 19 ], high–resolution detailed digital images [ 20 ] as well as a vast and ever–growing amount of data [ 17 ] are more readily available. Digital image processing is increasingly used to select and provide diagnostically important data [ 21 ] to avoid information noise that is difficult to evaluate.…”

“…As the development of the computer–aided detection of image differences is a multi–stage process [ 22 , 23 ], the first two steps towards achieving the main goal have already been attained. The first step, concerning the preliminary demonstration of the possibility of quantifying the texture features of horses’ incisor radiographs, has been previously published [ 17 ]. The second step is presented in this study.…”