• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.65 no.8
• /
• pp.1424-1429
• /
• 2016

Segmentation is one of the first steps in most diagnosis systems for characterization of dental caries in an early stage. The purpose of automatic dental cavity detection system is helping dentist to make more precise diagnosis. We proposed the semi-automatic method for the segmentation of dental caries on digital x-ray images. Based on a manually and roughly selected ROI (Region of Interest), it calculated the contour for the dental cavity. A snake algorithm which is one of active contour models repetitively refined the initial contour and self-examination and correction on the segmentation result. Seven phantom tooth from incisor to molar were made for the evaluation of the developed algorithm. They contained a different form of cavities and each phantom tooth has two dental cavities. From 14 dental cavities, twelve cavities were accurately detected including small cavities. And two cavities were segmented partly. It demonstrates the practical feasibility of the dental lesion detection using Computer-aided Detection (CADe).

• Progress in Medical Physics
• /
• v.26 no.1
• /
• pp.52-58
• /
• 2015

The automated dental cavity detection program for a new concept intra-oral dental x-ray imaging device, an auxiliary diagnosis system, which is able to assist a dentist to identify dental caries in an early stage and to make an accurate diagnosis, was to be developed. The primary theory of the automatic dental cavity detection program is divided into two algorithms; one is an image segmentation skill to discriminate between a dental cavity and a normal tooth and the other is a computational method to analyze feature of an tooth image and take an advantage of it for detection of dental cavities. In the present study, it is, first, evaluated how accurately the DRLSE (Direct Regularized Level Set Evolution) method extracts demarcation surrounding the dental cavity. In order to evaluate the ability of the developed algorithm to automatically detect dental cavities, 7 tooth phantoms from incisor to molar were fabricated which contained a various form of cavities. Then, dental cavities in the tooth phantom images were analyzed with the developed algorithm. Except for two cavities whose contours were identified partially, the contours of 12 cavities were correctly discriminated by the automated dental caries detection program, which, consequently, proved the practical feasibility of the automatic dental lesion detection algorithm. However, an efficient and enhanced algorithm is required for its application to the actual dental diagnosis since shapes or conditions of the dental caries are different between individuals and complicated. In the future, the automatic dental cavity detection system will be improved adding pattern recognition or machine learning based algorithm which can deal with information of tooth status.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.67 no.12
• /
• pp.1678-1684
• /
• 2018

Dental caries is one of the most popular oral disease. The aim of automatic dental cavity detection system is helping dentist to make accurate diagnosis. It is very important to separate cavity from the teeth in the detection system. In this paper, We compared two active contour algorithms, Snake and DRLSE(Distance Regularized Level Set Evolution). To improve performance, image is selected ROI(region of interest), then applied bilateral filter, Canny edge. In order to evaluate the algorithms, we applied to 7 tooth phantoms from incisor to molar. Each teeth contains two cavities of different shape. As a result, Snake is faster than DRLSE, but Snake has limitation to compute topology of objects. DRLSE is slower but those of performance is better.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.17 no.4
• /
• pp.715-722
• /
• 2022

If you don't regularly go to the dentist to check your teeth, it is difficult to notice cavities or various diseases of your teeth until you have pain or discomfort. Dental plaque is produced by the combination of food or foreign substances and bacteria in the mouth. Starch breaks down from the bacteria that form tartar. The acid that occurs at this time melts the enamel of the teeth and becomes a cavity. So tartar management is important. Poppyrin, the metabolism of bacteria in the mouth, reacts at 405 nm wavelengths and becomes red fluorescent, which can be seen by imaging through certain wavelength filters. By the above method, Frag and tartar are fluorescently detected and photographed with a yellow series of filters that pass wavelengths of 500 nm or more. It uses MATLAB to detect and display red fluorescence through image processing. Using the difference in voltage between normal teeth and tartar through an optical measuring circuit, it was connected to an Arduino and displayed on the LCD. This allows the user to know the presence and location of dental plaque more accurately.

• Journal of Dental Rehabilitation and Applied Science
• /
• v.26 no.3
• /
• pp.285-296
• /
• 2010

Robot-assisted illuminating equipment based on image-processing technology was developed and then its accuracy was measured. The current system was designed to detect facial appearance using a camera and to illuminate it using a robot-assisted system. It was composed of a motion control component, a light control component and an image-processing component. Images were captured with a camera and following their acquisition the images that showed motion change were extracted in accordance with the Adaboost algorithm. Following the detection experiment for the oral cavity of patients based on image-processing technology, a higher degree of the facial recognition was obtained from the frontal view and the light robot arm was stably controlled.

• Journal of Multimedia Information System
• /
• v.6 no.2
• /
• pp.81-86
• /
• 2019

Deep learning is emerging as one of the best tool in processing data related to medical imaging. In our research work, we have proposed a deep learning based framework CNN (Convolutional Neural Network) for the classification of dysplastic tissue images. The CNN has classified the given images into 4 different classes namely normal tissue, mild dysplastic tissue, moderate dysplastic tissue and severe dysplastic tissue. The dataset under taken for the study consists of 672 tissue images of epithelial squamous layer of oral cavity captured out of the biopsy samples of 52 patients. After applying the data pre-processing and augmentation on the given dataset, 2688 images were created. Further, these 2688 images were classified into 4 categories with the help of expert Oral Pathologist. The classified data was supplied to the convolutional neural network for training and testing of the proposed framework. It has been observed that training data shows 91.65% accuracy whereas the testing data achieves 89.3% accuracy. The results produced by our proposed framework are also tested and validated by comparing the manual results produced by the medical experts working in this area.

• Journal of the korean academy of Pediatric Dentistry
• /
• v.27 no.4
• /
• pp.490-493
• /
• 2000

Mesiodens are supernumerary teeth in the region of the maxillary central incisors, a condition which can lead to disorders of the dentition. Their presence may lead to disorders, such as delay in eruption of permanent teeth, development of dentigerous cysts, resorption of adjacent roots and eruption of a supernumerary tooth into nasal cavity. The optimal time for surgical intervention is controversial. Early diagnosis is important so as to enable good prognosis. Diagnosis primarily depends on x-ray films. Panorama film, occlusal film, periapical film have been used for detection of mesiodens. But, all of them have disadvantages. $SCANORA^{(R)}$ is a multimodal radiology system which utilizes the principles of narrow beam radiology and spiral tomography. Pre-programmed imaging procedure are provided for many dental situations An optional personal computer can be connected into the unit to help design the examination. We report two cases diagnosed by $SCANORA^{(R)}$. When compared with tube shift technique, it is simple and exact method of detecting mesiodens.