Neutrosophic Set-Based Caries Lesion Detection Method to Avoid Perception Error

Datta, Subhash C.; Chaki, Nabendu; Modak, Biswajit

doi:10.1007/s42979-020-0066-0

Cited by 7 publications

(7 citation statements)

References 29 publications

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“…The performance of this high resolution image reconstruction method is quite satisfactory. As the next phase of this work, authors are planning to localize the reconstruction mechanism within the region of interest [53], [54]. Consequently, the system itself will find the region of interest automatically for an unknown medical x-ray image.…”

Section: Discussionmentioning

confidence: 99%

A Granular Level Feature Extraction Approach to Construct HR Image for Forensic Biometrics Using Small Training DataSet

2020

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In forensic biometrics old x-ray images are often used for identification and verification. The number of homicide cases has increased manyfold along with population growth. Forensic odontology is a less expensive method to solve these cases. In forensic odontology, the old dental x-ray images of a victim are used as ante mortem data to compare the pattern for identification. The success of this post mortem biometrics process totally depends on the brain perception of forensic odontologist. Low resolution (LR) image may create human brain perception error at the time of making decision both in case of disease diagnosis and forensic biometrics. In such context, a software solution could help to reconstruct high resolution (HR) image from LR medical x-ray image. However, methods like convolution neural network (CNN) require high volume of training images to reconstruct HR images. Unfortunately, the available medical x-ray image repository does not offer a large volume of training dataset. This work aims to overcome this data related issue and presents a granular level feature based HR grayscale medical xray image reconstruction mechanism from LR image. This method uses machine learning for HR image reconstruction. The proposed granular level feature extraction method generates adequate amount of training set from limited amount of training image sets. These granular level features contain the influence of neighboring points separately and its direction. These features are highly immune to noises and preserve important properties like edges. Polynomial regression model is used for HR value reconstruction. This image reconstruction provides satisfactory results for image database like dental radiographs datasets and the average structural similarity index (SSIM) metric reaches 0.9326.

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Section: Discussionmentioning

confidence: 99%

A Granular Level Feature Extraction Approach to Construct HR Image for Forensic Biometrics Using Small Training DataSet

2020

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“…The limitation of this approach is that it does not work well for poor-quality pictures, which leads to inappropriate feature extraction. In Datta, Chaki & Modak (2020) , a method reduced the computational efforts and caries region identified in optimum time. The X-ray image is processed in the neutrosophic domain to identify the suspicious part, and an active contour method is employed to detect the outer line of the carious part.…”

Section: Methodsmentioning

confidence: 99%

“…PPV (Hosntalab et al, 2010;Mortaheb, Rezaeian & Soltanian-Zadeh, 2013;Berdouses et al, 2015;Datta, Chaki & Modak, 2020) Huang & Hsu, 2008;Olsen et al, 2009;Banu et al, 2014;Nuansanong, Kiattisin & Leelasantitham, 2014;Ghaedi et al, 2014;Lin et al, 2014;Datta & Chaki, 2015a,b;Poonsri et al, 2016;Rad et al, 2018;Osterloh & Viriri, 2019;Datta, Chaki & Modak, 2019Devi, Banumathi & Ulaganathan, 2019;Kumar, Bhadauria & Singh, 2020) Mahalanobis distance MHD Hausdorff distance HD (Abdi, Kasaei & Mehdizadeh, 2015) Distance vector DV (Prajapati, Desai & Modi, 2012) Similarity measure SM Alsmadi, 2018;Singh & Agarwal, 2018) The area under ROC curve AUC (Nuansanong, Kiattisin & Leelasantitham, 2014) Cohens kappa coefficient KAP (Berdouses et al, 2015) Mean absolute error MAE (Vijayakumari et al, 2012;Amer & Aqel, 2015;Tuan et al, 2018;Kumar, Bhadauria & Singh, 2020) Mean square error MSE (Vijayakumari et al, 2012;Singh & Agarwal, 2018;Tuan et al, 2018) Error rate ERR (Zhou & Abdel-Mottaleb, 2005;Nomir & Abdel-Mottaleb, 2008;Hosntalab et al, 2010;Lira et al, 2014;Datta & Chaki, 2015b;Purnama et al, 2015;Tuan et al, 2018;…”

Section: Confusion Matrixmentioning

confidence: 99%

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Descriptive analysis of dental X-ray images using various practical methods: A review

Kumar¹,

Bhadauria²,

Singh³

2021

PeerJ Computer Science

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In dentistry, practitioners interpret various dental X-ray imaging modalities to identify tooth-related problems, abnormalities, or teeth structure changes. Another aspect of dental imaging is that it can be helpful in the field of biometrics. Human dental image analysis is a challenging and time-consuming process due to the unspecified and uneven structures of various teeth, and hence the manual investigation of dental abnormalities is at par excellence. However, automation in the domain of dental image segmentation and examination is essentially the need of the hour in order to ensure error-free diagnosis and better treatment planning. In this article, we have provided a comprehensive survey of dental image segmentation and analysis by investigating more than 130 research works conducted through various dental imaging modalities, such as various modes of X-ray, CT (Computed Tomography), CBCT (Cone Beam Computed Tomography), etc. Overall state-of-the-art research works have been classified into three major categories, i.e., image processing, machine learning, and deep learning approaches, and their respective advantages and limitations are identified and discussed. The survey presents extensive details of the state-of-the-art methods, including image modalities, pre-processing applied for image enhancement, performance measures, and datasets utilized.

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“…Most researchers judge dental caries by X-ray images 2,3 . Our dataset is taken by the patient himself, and the images are labeled by professional doctors.…”

Section: Introductionmentioning

confidence: 99%

Detection of Children’s Molars Based on Noise Filter

Yan

Chen

Song

et al. 2022

Preprint

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Pit and fissure sealant is for children aged seven to twelve years to prevent molars from becoming caries. In this paper, we propose a new detection framework to identify whether children need pit and fissure sealing. We divide the framework into two parts: molar detection and molar classification. According to the characteristics of teeth, we propose to use the clustering method to filter the bounding box in the object detection part. In the region divided by clustering, we only keep one detection frame in the same category. In the classification part, we propose a noise filtering layer based on wavelet transform for feature extraction. We map the training samples to another space in the training process based on metric learning to increase the distance between categories and improve the accuracy of classification.

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Neutrosophic Set-Based Caries Lesion Detection Method to Avoid Perception Error

Cited by 7 publications

References 29 publications

A Granular Level Feature Extraction Approach to Construct HR Image for Forensic Biometrics Using Small Training DataSet

A Granular Level Feature Extraction Approach to Construct HR Image for Forensic Biometrics Using Small Training DataSet

Descriptive analysis of dental X-ray images using various practical methods: A review

Detection of Children’s Molars Based on Noise Filter

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