Semi-supervised OTSU based hyperbolic tangent Gaussian kernel fuzzy C-mean clustering for dental radiographs segmentation

Kumar, Anuj; Bhadauria, H.S.; Singh, Annapurna

doi:10.1007/s11042-019-08268-8

Cited by 17 publications

(9 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It shows good accuracy and optimum results as compared to Jain & Chauhan (2017) . Furthermore, various approaches were used to automatically detect teeth structures ( Huang & Hsu, 2008 ; Sattar & Karray, 2012 ; Niroshika, Meegama & Fernando, 2013 ; Nuansanong, Kiattisin & Leelasantitham, 2014 ; Kumar, Bhadauria & Singh, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

“…(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%

“…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%

“…The dental X-ray image analysis methods can be categorized in several categories: region growing techniques, edge detection methods, thresholding based, clustering techniques, level set, and active contour, etc., are presented in ‘Image processing methods for dental image analysis’ ( Mahoor & Abdel-Mottaleb, 2004 ; Zhou & Abdel-Mottaleb, 2005 ; Nomir & Abdel-Mottaleb, 2005 , 2007 ; Gao & Chae, 2008 ; Oprea et al, 2008 ; Patanachai, Covavisaruch & Sinthanayothin, 2010 ; Harandi & Pourghassem, 2011 ; Hu et al, 2014 ; Amer & Aqel, 2015 ; Zak et al, 2017 ; Avuçlu & Bacsçiftçi, 2020 ) ( Rad et al, 2015 ; Tuan, Ngan & Son, 2016 ; Poonsri et al, 2016 ; Son & Tuan, 2016 , 2017 ; Ali et al, 2018 ; Alsmadi, 2018 ; Obuchowicz Rafałand Nurzynska et al, 2018 ; Tuan et al, 2018 ; Fariza et al, 2019 ; Kumar, Bhadauria & Singh, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…N.A Caries detection (Kumar, Bhadauria & Singh, 2020) The various dental structures were separated using the fuzzy C-means algorithm and the hyperbolic tangent gaussian kernel function.…”

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

Self Cite

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

Section: Methodsmentioning

confidence: 99%

Section: Pre-processing Techniquesmentioning

confidence: 99%

Section: Confusion Matrixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…N.A Caries detection (Kumar, Bhadauria & Singh, 2020) The various dental structures were separated using the fuzzy C-means algorithm and the hyperbolic tangent gaussian kernel function.…”

mentioning

confidence: 99%

See 3 more Smart Citations

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

Kumar¹,

Bhadauria²,

Singh³

2021

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

A comparative experimental analysis and deep evaluation practices on human bone fracture detection using x‐ray images

Kumar

Prabu

Pandimurugan

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

Summary X‐ray images are widely used to identify fractures in human bones. Radiographic diagnosis models take more time and manual procedures for establishing physical analysis of bone fractures. Furthermore, the lack of clinical resources and the medical support systems lead to inaccurate bone data extractions. In this case, the need for detail is required to understand the scientific issues in x‐ray and magnetic resonance imaging (MRI) based bone fracture diagnosis solutions. Particularly, the current bone fracture detection models are emerging with computerized frameworks and health informatics consoles. In this regard, this article lists five phases. First, data preparation and data collection tasks are initiated. Second, bone fracture diagnosis models are comparatively analyzed to find optimal observations. Consequently, the third phase of the article examines the treatments against bone fracture observations with traditional and deep learning (DL) techniques. The fourth phase consists of relative diagnosis solutions and the treatment benefits on various types of technical frameworks. Finally, this work concludes the comparison with recent DL‐based bone fracture identification models.

show abstract

A Survey on Image Processing Techniques for Detection of Cavities in Dental X-ray Images

Geethasree¹,

Sri²,

Sravani³

et al. 2023

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Semi-supervised OTSU based hyperbolic tangent Gaussian kernel fuzzy C-mean clustering for dental radiographs segmentation

Cited by 17 publications

References 32 publications

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

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

A comparative experimental analysis and deep evaluation practices on human bone fracture detection using x‐ray images

A Survey on Image Processing Techniques for Detection of Cavities in Dental X-ray Images

Contact Info

Product

Resources

About