Identification of pulpitis at dental X-ray periapical radiography based on edge detection, texture description and artificial neural networks

Tumbelaka, Bernard Y; Oscandar, Fahmi; Baihaki, Faisal Nur; Sitam, Suhardjo; Rukmo, Mandojo

doi:10.4103/1658-5984.138139

Cited by 11 publications

(5 citation statements)

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“…In a similar work by Tumbelaka et al, pulpitis was identified from Periapical Radiograph using the Gaussian filter, texture descriptors and Artificial Neural Network analysis. This method diagnosed the pulp indications as normal, pulpitis and death tooth but the authors felt that the radiography reading has to be digitized to get better diagnosis validation [24]. The proposed method achieved the accuracy of 91.09% which is better compared to the previous methodologies and can be used as an assistive tool by the Dentist in diagnosing and treatment of Pulpitis from Dental Panoramic Radiograph.…”

Section: Algorithm-3mentioning

confidence: 99%

Diagnosis of Pulpitis from Dental Panoramic Radiograph Using Histogram of Gradients with Discrete Wavelet Transform and Multilevel Neural Network Techniques

Vigil¹,

Bharathi²

2021

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Radiograph plays the major role of diagnosis, treatment and surgery in the Dental field. There are many types of Intra and extra oral radiographs in which Dental Panoramic Radiograph helps in visualising the full view of the oral cavity. Pulpitis is the dental diseases caused due to the inflammation of the dental pulp from untreated caries, trauma or multiple restorations which leads to Apical Periodontitis. To predict the severity of pulp vitality pulp inflammation has to be evaluated. Radiographs helps the dentist in diagnosing the extent of tooth decay and inflammation. An automatic diagnostic model is proposed using robust algorithms to diagnose pulpits. Dental Panoramic Radiograph is used in the proposed research to diagnose the pulpitis and to classify the normal teeth from the pulpitis. The collected images are pre-processed using Histogram Equalization and filtered using Gaussian and Median filters. Modified K-Means algorithm is applied to segment the bony and teeth area from the oral cavity area. Integral Histogram of Gradients with Discrete Wavelet Transform feature extraction techniques and Multi-Layer Neural Network Classifier is proposed to achieve the accuracy of 91.09% which can be used as an assistive tool by the dentist to diagnose pulpitis.

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Section: Algorithm-3mentioning

confidence: 99%

Diagnosis of Pulpitis from Dental Panoramic Radiograph Using Histogram of Gradients with Discrete Wavelet Transform and Multilevel Neural Network Techniques

Vigil¹,

Bharathi²

2021

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“…e transformation formula is shown in formula (2). If it is a color map of other color space, you can first map it to RGB color space and then choose a method to grayscale [9].…”

Section: Canny Edge Detectionmentioning

confidence: 99%

“…erefore, a group of professional and high-quality intelligent design identi cation personnel are needed [7,8]. In order to meet the needs of socialist economic construction with Chinese characteristics, professional talents need to have good professional ethics, understand the basic working principle, structure, form, and function of the software console, master software fault diagnosis [9,10], detection, intelligent pattern recognition, and other related theories, and computer skills and knowledge are particularly rare.…”

Section: Introductionmentioning

confidence: 99%

Research on Edge Detection Algorithm of Indoor Soft Decoration Pattern

Zheng

Wang

2022

Mobile Information Systems

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Purpose of edge detection is to identify the points with obvious brightness change in digital image. The image edge detection effect under normal light is good, but the image display effect under dark vision is poor, and most of the edge information is lost. In order to quickly and accurately obtain the image edge part containing a large amount of feature information, many classical algorithms have appeared in the field of edge detection. This paper discusses the development of soft edge detection and intelligent edge detection technology for readers. Aiming at the image difference caused by proportion, rotation, and folding in the process of automatic recognition of indoor soft decoration patterns, this paper focuses on the collection of proportion, rotation, blur, and illumination of indoor soft decoration pattern images under five folding changes. The edge detection algorithm (EDA) is used to quickly and accurately obtain images containing a large amount of feature information at a low cost of edge parts, extract the local features of the pattern, calculate the feature matching with Euclidean distance, and eliminate the false matching with random sampling consistency algorithm. Finally, the experimental results show that EDA algorithm usually combines a variety of features to realize edge detection, and its output edge detection result is better than the traditional edge detection algorithm which only considers the gradient change. The algorithm has the highest accurate matching rate, and the average accurate matching rate is 87.10%; the robustness of this algorithm is better than other algorithms, and the speed is the fastest. Therefore, EDA algorithm has good applicability in indoor soft decoration pattern feature matching.

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“…This showed that pulpitis has become the most common cause of toothache followed by death of the pulp and spread of infection through the apical foramina into the periapical tissues and this could be found among all ages especially younger people due to several unhygienic factors like smoke, hormonal disturbance in woman, diabetes, stress, cancer, AIDS, genetic factors, insufficiently nutrients, and drugs 17 .…”

Section: Fig 3: Radiographic Evaluation Of Pulpal and Periapical Tissuementioning

confidence: 99%

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2016

IJPSR

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Identification of pulpitis at dental X-ray periapical radiography based on edge detection, texture description and artificial neural networks

Cited by 11 publications

References 0 publications

Diagnosis of Pulpitis from Dental Panoramic Radiograph Using Histogram of Gradients with Discrete Wavelet Transform and Multilevel Neural Network Techniques

Diagnosis of Pulpitis from Dental Panoramic Radiograph Using Histogram of Gradients with Discrete Wavelet Transform and Multilevel Neural Network Techniques

Research on Edge Detection Algorithm of Indoor Soft Decoration Pattern

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