A Simple and Novel CBIR Technique for Features Extraction Using AM Dental Radiographs

Prajapati, Dharmesh B.; Desai, Nirav P.; Modi, Chintan K.

doi:10.1109/csnt.2012.50

Cited by 4 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various filters have been used to achieve the best potential outcome for the irregularities present in dental images like Average filter, Bilateral filter, Laplacian filter, Homomorphic filter, and Butterworth filter, Median Gaussian filter, and Weiner filter. In recent studies, various filtering techniques used by researchers but widely used filtering methods are Gaussian filter and the median filter, which shows the best result ( Benyó et al, 2009 ; Prajapati, Desai & Modi, 2012 ; Nuansanong, Kiattisin & Leelasantitham, 2014 ; Razali et al, 2014 ; Datta & Chaki, 2015a , b ; Rad et al, 2015 ; Tuan, Ngan & Son, 2016 ; Jain & Chauhan, 2017 ; Alsmadi, 2018 ). However, the drawback of the median filter is that it degrades the boundary details.…”

Section: Methodsmentioning

confidence: 99%

Section: Performance Measuresmentioning

confidence: 99%

“…Whereas the (Lai & Lin, 2008) Adaptive local contrast stretching is used to make the tooth region smoother after that, adaptive morphological enhancement is applied to improve the texture values. (Prajapati, Desai & Modi, 2012) A median filter is used to eradicate picture impulse noise. (Mahoor & Abdel-Mottaleb, 2004;Zhou & Abdel-Mottaleb, 2005;Huang et al, 2012) Top hat and bottom hat filters are applied where the teeth become brightened, and the bone and shadow regions obscured.…”

Section: Pre-processing Techniquesmentioning

confidence: 99%

“…Here, FF & FD give small values for matching Euclidean distance and absolute distance, indicating that the performance is better than the other two methods. 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%

“…Missing teeth detection (Prajapati, Desai & Modi, 2012) A region growing technique is applied to the X-rays to extract the tooth; then, the content-based image retrieval (CBIR) technique is used for matching purposes.…”

mentioning

confidence: 99%

See 4 more Smart Citations

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

Kumar¹,

Bhadauria²,

Singh³

2021

PeerJ Computer Science

View full text Add to dashboard Cite

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.

show abstract