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马牙受 EOTRH 综合征影响的 X 光图像的滤波和图像纹理分析选择。

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

机构信息

Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland.

Institute of Biomedical Engineering, Faculty of Mechanical Engineering, Białystok University of Technology, 15-351 Bialystok, Poland.

出版信息

Sensors (Basel). 2022 Apr 11;22(8):2920. doi: 10.3390/s22082920.

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 , , , , , , , , and filters independently, and 93 features of image texture were extracted using (FOS), (GLCM), (NGTDM), (GLDM), (GLRLM), and (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.

摘要

马的牙吸收性过度增生症(EOTRH)是一种牙科疾病,主要影响门齿。随着老年马匹发病率的增加和疾病的疼痛性进展过程,需要提高早期诊断水平。除了临床发现,EOTRH 的识别还基于典型的放射影像学发现,包括牙齿吸收和过度增生的程度。本研究旨在介绍数字处理方法,以识别马匹牙齿放射图像中与疾病进展相关的纹理特征。从 80 匹马的上颌门齿获得了放射图像。每个门齿都用单独的掩模进行注释,并根据临床分类为 0、1、2 或 3 度 EOTRH。图像分别用 、 、 、 、 、 、 和 滤波器进行过滤,并使用 (FOS)、 (GLCM)、 (NGTDM)、 (GLDM)、 (GLRLM)和 (GLSZM)方法提取 93 个图像纹理特征。选择了最具信息量的处理方法。GLCM 和 GLRLM 为 EOTRH 综合征的放射影像学征象的定量评估提供了最有利的特征,通过过滤 滤波器可以改善边缘界定,从而支持这些特征。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63a7/9030967/334e3e2b154a/sensors-22-02920-g001.jpg

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