Dental classification for periapical radiograph based on multiple fuzzy attribute

Tangel, Martin Leonard; Fatichah, Chastine; Yan, Fei; Betancourt, Janet Pomares; Widyanto, M. Rahmat; Dong, Fangyan; Hirota, Kaoru

doi:10.1109/ifsa-nafips.2013.6608417

Cited by 18 publications

(11 citation statements)

References 9 publications

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“…The results comparison was performed against different caries detection diagnostic methods to determine if the results fell within acceptable limits or not. The caries detection diagnostic methods compared against include textural classification as discussed by [21], dental classification for periapical radiographs by [22] and caries detection through a supervised learning model proposed by [23] for panoramic images. The comparison of our proposed method with other detection techniques is shown in Table 1.…”

Section: Resultsmentioning

confidence: 99%

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Automatic Blob Detection for Dental Caries

Majanga

Viriri

2021

Applied Sciences

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Dental Caries are one of the most prevalent chronic diseases around the globe. Detecting carious lesions is a challenging task. Conventional computer aided diagnosis and detection methods in the past have heavily relied on the visual inspection of teeth. These methods are only effective on large and clearly visible caries on affected teeth. Conventional methods have been limited in performance due to the complex visual characteristics of dental caries images, which consist of hidden or inaccessible lesions. The early detection of dental caries is an important determinant for treatment and benefits much from the introduction of new tools, such as dental radiography. In this paper, we propose a deep learning-based technique for dental caries detection namely: blob detection. The proposed technique automatically detects hidden and inaccessible dental caries lesions in bitewing radio-graphs. The approach employs data augmentation to increase the number of images in the data set to have a total of 11,114 dental images. Image pre-processing on the data set was through the use of Gaussian blur filters. Image segmentation was handled through thresholding, erosion and dilation morphology, while image boundary detection was achieved through active contours method. Furthermore, the deep learning based network through the sequential model in Keras extracts features from the images through blob detection. Finally, a convexity threshold value of 0.9 is introduced to aid in the classification of caries as either present or not present. The process of detection and classifying dental caries achieved the results of 97% and 96% for the precision and recall values, respectively.

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Section: Resultsmentioning

confidence: 99%

“…Caries detection in panoramic images [23] 1392 98.0% Dental classification for periapical images [22] 78 82.5% Textural classification of digital images [21] 64 96.88% PaxNet [11] 470 86.05% ICDAS [12] 620 92.37% GLCM [13] 240 80% Deep learning [16] 304 64.14% Proposed approach 11,114 97.0%…”

Section: Dataset Images Accuracymentioning

confidence: 99%

Automatic Blob Detection for Dental Caries

Majanga

Viriri

2021

Applied Sciences

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“…The automatic tooth segmentation based on radiographic data is very challenging due to noise, low contrast, uneven illumination, the complexity of the object topology in the image, random tooth orientation, and the absence of a clear line of demarcation between the tooth and other tissues [7]. Dental X-ray image segmentation falls under machine learning theory, as application of a clustering technique that groups similar values into one group and different values into different groups [8].…”

Section: Introductionmentioning

confidence: 99%

Segmenting Tooth Components in Dental X-Ray Images Using Gaussian Kernel- Based Conditional Spatial Fuzzy C-Means Clustering Algorithm

Fariza¹,

Arifin²,

Astuti³

et al. 2019

IJIES

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Tooth component segmentation is a crucial task in computer-aided design for forensic odontology, especially to estimate chronological age. Tooth segmentation on radiographic data is a very challenging task due to noise, low contrast, and uneven illumination. The Fuzzy C-Means clustering is generally used for image segmentation that allow pixels to be classified into one or more clusters according to their membership value. However, this clustering method still has problems associated with the shifting of cluster centers and sensitivity to the overlapping intensity distributions between classes. This paper proposes a modified strategy of the conditional spatial Fuzzy C-Means (csFCM) that incorporates global and spatial information into a weighted membership function by replacing the Euclidean distance with the Gaussian kernel distance to increase insensitivity to noise and outliers. The aim of this paper is to divide the tooth into 3 components, i.e. enamel, dentine, and pulp. Therefore, this modified algorithm is preceded by dental X-ray image pre-processing and continued by combining each dental component clusters into one composite image. The tooth image is pre-processed using Contrast Limited Adequate Histogram Equalization (CLAHE) and gamma adjustment to enhance the dental X-ray images quality from the non-uniform lighting. The Gausian kernel-based conditional spatial Fuzzy C-Means (GK-csFCM) segments the dental image into four class clusters, namely enamel, dentine, pulp, and background. Through iterations, the resulting cluster centers are more convergent with real cluster centers, thus ensuring the proposed method improves the drawback of inherent FCM-based methods and further promoting image segmentation performance. The experimental results on the real dental X-ray images showed that GK-csFCM has better performance than the typical FCM and csFCM clustering algorithms in terms of both qualitative and quantitative metrics, i.e. accuracy, specificity, sensitivity, and precision.

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“…FCM determines tooth status by cyclic updating membership values and prototypes until stable. Tangel, Fatichah [7] presented a fuzzy inference system to achieve four categories of dental images. This system used Mamdani type FIS with centroid defuzzification method which employed multiple fuzzy attributes and the selected threshold is 0.15.…”

Section: Introductionmentioning

confidence: 99%

Teeth Classification Based on Haar Wavelet Transform and Support Vector Machine

Liu¹,

Li²,

Quinn³

2018

Proceedings of the 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)

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To improve the efficiency of stomatology practitioners, this paper proposed a novel teeth type classification method. Our method was based on three successful components: Haar wavelet transform, principal component analysis, and support vector machine. We create a 120-image dataset, with 30 images for incisor, canine, premolar, and molar. The results showed our method achieved an overall classification accuracy of 81.83± 1.79%, better than decision tree and multilayer perceptron methods.

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Dental classification for periapical radiograph based on multiple fuzzy attribute

Cited by 18 publications

References 9 publications

Automatic Blob Detection for Dental Caries

Automatic Blob Detection for Dental Caries

Segmenting Tooth Components in Dental X-Ray Images Using Gaussian Kernel- Based Conditional Spatial Fuzzy C-Means Clustering Algorithm

Teeth Classification Based on Haar Wavelet Transform and Support Vector Machine

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