Multiscale image analysis for the quantitative evaluation of periapical lesion healings

Kim, Desok; Jeong, Hyekyung; Kim, Minjin; Kim, Changick; Lee, Byung‐Do

doi:10.1109/bmei.2010.5639554

Cited by 2 publications

(1 citation statement)

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“…In another study, after some classic image processing methods (top hat, erosion, and opening) were performed, the skeleton was extracted, and textural features were calculated for the region of interest. A pair of pre-treatment and post-treatment values was then tested in an evaluation of the healing process [33]. The scope of such studies has typically been the detection of areas of alveolar bone on periapical dental radiographs [12][13][14][15].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2019

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The aim of this study was to examine whether additional digital intraoral radiography (DIR) image preprocessing based on textural description methods improves the recognition and differentiation of periapical lesions. (1) DIR image analysis protocols incorporating clustering with the k-means approach (CLU), texture features derived from co-occurrence matrices, first-order features (FOF), gray-tone difference matrices, run-length matrices (RLM), and local binary patterns, were used to transform DIR images derived from 161 input images into textural feature maps. These maps were used to determine the capacity of the DIR representation technique to yield information about the shape of a structure, its pattern, and adequate tissue contrast. The effectiveness of the textural feature maps with regard to detection of lesions was revealed by two radiologists independently with consecutive interrater agreement. (2) High sensitivity and specificity in the recognition of radiological features of lytic lesions, i.e., radiodensity, border definition, and tissue contrast, was accomplished by CLU, FOF energy, and RLM. Detection of sclerotic lesions was refined with the use of RLM. FOF texture contributed substantially to the high sensitivity of diagnosis of sclerotic lesions. (3) Specific DIR texture-based methods markedly increased the sensitivity of the DIR technique. Therefore, application of textural feature mapping constitutes a promising diagnostic tool for improving recognition of dimension and possibly internal structure of the periapical lesions.

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