Dental cyst diagnosis using texture analysis

Vijayakumari, B.; Ulaganathan, G; Banumathi, A.; Banu, A. Farzana Shahar; Kayalvizhi, M.

doi:10.1109/mvip.2012.6428774

Cited by 9 publications

(11 citation statements)

References 6 publications

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“…The method of using texture feature maps obtained by texture analysis of the DIR images used in the current study is consistent with that used in some previous studies [19,20]. Those methods are applicable to wide range of problems e.g., for identification of macerals [28], defect detection [29].…”

Section: Discussionsupporting

confidence: 69%

“…Other applications of texture analysis were used for periapical bone healing [16][17][18], where radiological assessment of treatment effectiveness of guided bone regeneration was measured. The most similar research to the presented is considered in [19,20], where the authors tried to detect the type of cyst using the Gray-Level Co-occurrence Matrix and its related properties. By using textural analysis to enhance bone representation derived from DIR images, trabecular structure may be depicted more informatively, and the shapes of different anatomical structures may be determined more accurately.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionsupporting

confidence: 69%

Section: Introductionmentioning

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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“…Texture feature analysis was also applied to evaluation of the bone structure affected by osteoporosis [23][24][25], and similar techniques were used to characterize periapical bone loss [26][27][28][29], periapical bone healing [30][31][32][33], and focal periapical lesions [34,35]. Other research has focused on evaluation of the jaws [36] and cases in which sex-dependent structural differences in the jaw bone are visible [37].…”

Section: Related Workmentioning

confidence: 99%

Caries detection enhancement using texture feature maps of intraoral radiographs

et al. 2018

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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.

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“…Feature extraction is the process of analysing the texture of an image. Texture is a repeated pattern of local variation in image intensity [13]. GLCM is one of the popular methods of estimating textural information present in the images.…”

Section: Related Workmentioning

confidence: 99%

Textural Analysis Based Classification of Digital X-ray Images for Dental Caries Diagnosis

Geetha¹,

S²

2019

IJEM

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In this paper, we propose a suitable textural feature for diagnosis of dental caries in digital radiographs. The dental diagnosis system consists of Laplacian filter for image sharpening, adaptive threshold and morphological operations for segmentation, and support vector machine (SVM) as a classifier. In segmented image, textural features are extracted, and applied to the classifier, to classify the image as caries or normal. Experimental results indicate that GLCM (Grey Level Co-occurrence Matrix) and GLDM (Grey Level Difference Method) textural features are giving better performance measures as compared to other types of textural features with an accuracy of 96.88%, sensitivity of 1, specificity of 0.8667 and precision of 96.08%. The data were analyzed by Analysis of Variance (ANOVA), at a significant level of 5%. This result indicates that the interaction of feature extraction methods on performance measures are significant. Hence, GLCM or GLDM features provide reliable decision support for dental caries diagnosis.

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Dental cyst diagnosis using texture analysis

Cited by 9 publications

References 6 publications

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

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

Caries detection enhancement using texture feature maps of intraoral radiographs

Textural Analysis Based Classification of Digital X-ray Images for Dental Caries Diagnosis

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