A Novel Technique to Detect Caries Lesion Using Isophote Concepts

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

doi:10.1016/j.irbm.2019.04.001

Cited by 21 publications

(12 citation statements)

References 24 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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“…However, this approach was applied to the dataset of 10 patients with confirmed caries. A geodesic contour technique ( Datta, Chaki & Modak, 2019 ) shows better computational time results than multilevel thresholding, watershed, and level set. The limitation of this approach is that it does not work well for poor-quality pictures, which leads to inappropriate feature extraction.…”

Section: Methodsmentioning

confidence: 99%

“…(Singh & Agarwal, 2018) Median filtering is used to lower noise, and an unsharp marking filter is used to enhance the high-frequency component. (Datta, Chaki & Modak, 2019) Local averaging is used to eliminate noisy features. (Kumar, Bhadauria & Singh, 2020) The guided filter is applied with a window size of 3 * 3 and is cast-off towards calculating output pixel size.…”

Section: Pre-processing Techniquesmentioning

confidence: 99%

“…In another approach (Prajapati, Desai & Modi, 2012), feature vectors are evaluated and used to find the image distance vector (D n ) using formula: D n ¼ P T n FV À FVQ j j , where feature vector (TnFV) is used for database image and (FVQ) is used for the query image. TPR (Hosntalab et al, 2010;Mortaheb, Rezaeian & Soltanian-Zadeh, 2013;Ghaedi et al, 2014;Abdi, Kasaei & Mehdizadeh, 2015;Berdouses et al, 2015;Datta & Chaki, 2015b;Alsmadi, 2018;Datta, Chaki & Modak, 2019 True negative rate (specificity) TNR (Hosntalab et al, 2010;Mortaheb, Rezaeian & Soltanian-Zadeh, 2013;Ghaedi et al, 2014;Abdi, Kasaei & Mehdizadeh, 2015;Berdouses et al, 2015;Datta & Chaki, 2015b;Alsmadi, 2018;Datta, Chaki & Modak, 2019) Positive predictive value (precision)…”

Section: Confusion Matrixmentioning

confidence: 99%

“…3 Cyst detection (Datta, Chaki & Modak, 2019) The geodesic active contour method is applied to identify the dental caries lesion.…”

mentioning

confidence: 99%

See 2 more Smart Citations

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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A Systematic Review on the Evolution of Dental Caries Detection Methods and Its Significance in Data Analysis Perspective

Datta

Chaki

Modak³

2020

Intelligent Data Analysis

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A Novel Technique to Detect Caries Lesion Using Isophote Concepts

Cited by 21 publications

References 24 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

A Systematic Review on the Evolution of Dental Caries Detection Methods and Its Significance in Data Analysis Perspective

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