Texture based classification of dental cysts

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

doi:10.1109/iccicct.2014.6993152

Cited by 5 publications

(6 citation statements)

References 5 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: 70%

“…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: 70%

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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“…Vertical root fracture [72,73] Deep learning Periapical pathosis [21], dental tumors [74], tooth numbering [75][76][77][78], tooth detection and identification [79][80][81], periodontal bone loss [32,82,83] Disease classification Classical image analysis approaches Tooth detection [84,85], osteoporosis assessment [86], dental caries [87] Machine learning Dental caries [88], proximal dental caries [14], molar and pre-molar teeth [89], osteoporosis [90], dental caries [15], periapical lesions [16,17], dental restorations [22], periapical roots [91], teeth with root [92], sagittal patterns [93] Deep learning Tooth numbering [94][95][96][97][98][99], dental implant stages [100], implant fixture [101], bone loss [18], periapical periodontitis [102][103][104][105], dental decay [106], approximal dental caries [19] Disease segmentation C...…”

Section: Disease Detection Machine Learningmentioning

confidence: 99%

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

et al. 2023

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Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues.

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Texture based classification of dental cysts

Cited by 5 publications

References 5 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

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

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