Panoramic Dental Radiography Image Enhancement Using Multiscale Mathematical Morphology

Román, Julio César Mello; Fretes, Vicente; Adorno, Carlos G.; Silva, Ricardo Gariba; Noguera, José Luis Vázquez; Legal-Ayala, Horacio; Mello-Román, Jorge Daniel; Torres, Ricardo Daniel Escobar; Facon, Jacques

doi:10.3390/s21093110

Cited by 28 publications

(15 citation statements)

References 44 publications

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“…The networks included 18 predicted interactions (Figure 2). Christ-Siemens-Touraine syndrome (#305100) Permanent 18 12,14, 15, 18, 22, 24, 25, 28, 31, 32, 34, 35, 38, 41, 42, 44, 45, 48 Abs, Raes and Vercruysse (1994) Wolff-Parkinson-White syndrome (#194200) Permanent 15 14, 15, 17, 18, 12, 22, 25, 27, 28, 35, 37, 38, 45, 47, 48 Aditya, Lele and Aditya (2011) Fahr's syndrome (#213600) Permanent 16 11,14, 15, 17, 21, 35, 36, 37, 38, 42, 43, 44, 45, 46, 47, 48 Agarwal et al (2014) Polycistic ovarian syndrome (#184700) Permanent 26 12,13,14,15,16,17,18,22,23,25,27,28,31,32,33,34,35,37,38,41,42,44,45,46,47,48 Aminabadi, Ebrahii and Oskouei (2010) Ellis-van Creveld syndrome (#225500) Primary / Permanent 6 / 9 52, 53, 63, 71, 73, 83 / 12, 31, 32, 33, 35, 41, 42, 43, 45 Ann Drum et al (1985) Rieger syndrome (#602482) Permanent 19 11, 12, 13, 14, 16, 18, 21, 22, 23, 26, 28, 31, 32, 33, 36, 41, 42, 45, 46 Ardila and Álvarez-Martínez (2022) Axenfeld-Rieger syndrome (#601499, #180500, #602482) Permanent 22 16, 15, 14, 13, 12, 21 ,22, 25, 26, 31, 32, 33, 34, 35, 36, 37, 41, 42, 43, 44, 45, 46 Arora et al (2016) Witkop syndrome (#189500) Permanent 26 11,12,14,15,16,…”

Section: Resultsmentioning

confidence: 99%

“…51, 52, 54, 61, 62, 64, 74, 84 / 11, 12, 13, 14, 15, 21, 22, 23, 24, 25, 33, 34, 43, 44 4. 54, 62, 63, 64, 71, 81, 84 / 11, 12, 14, 15, 21, 22, 23, 24, 25, 31, 32, 33, 35, 41, 42, 43, 44 Barber et al (2012) Carvajal syndrome (#605676) Permanent 13 15, 17, 18, 24, 25, 27, 28, 35, 37, 38, 45, 47, 48 Bekiesinska-Figatowska et al (2010) Hypomyelination, hypogonadotropic hypogonadism, hypodontia syndrome (4H) (#612440) Permanent 8 12, 15, 22, 24, 34, 35, 44, 45 Bergendal (2001) Hypohidrotic ectodermal dysplasia (#305100) Permanent 16 13, 14, 15, 24, 25, 31, 32, 33, 34, 35, 38, 41, 42, 43, 44, 45 Bergendal et al (2015) X-linked hypohidrotic ectodermal dysplasia (#300291) Permanent 28 11,12,14,15,17,18,21,22,24,25,27,28,31,32,33,34,35,36,37,38,41,42,43,44,45,46,47,48 Bildik et al ( 2012) Hypohidrotic ectodermal dysplasia (#305100) Permanent 30 12, 13, 14, 15, 16, 17, 18, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 41, 42, 43, 44, 45, 46, 47, 48 Blankenstein et al (2001) Carpenter syndrome (#201000) Permanent 22 12, 14, 15, 17, 18, 22, 24, 25, 27, 28, 31, 32, 34, 35, 37, 38, 41, 42, 44, 45, 47, 48 Cagetti et al (2019) Progeroid syndrome (#612289) Permanent 26 11, 12, 14, 15, 16, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28, 33, 34, 36, 37, 38, 43, 44, 45, 46, 47, 48 Callanan, Anand and Sheehy (2006) Sotos syndrome (#117550) Permanent 15…”

Section: Resultsmentioning

confidence: 99%

“…Thus, radiographic and molecular diagnosis may be necessary. Analysis of dental radiographs is an important part of the diagnostic process in daily clinical practice, and interpretation by an expert includes teeth detection and numbering [34][35][36]. In the reading of the articles included, panoramic radiographs with poor quality were evidenced, which can affect the diagnosis and consequent interpretation of the case report.…”

Section: Discussionmentioning

confidence: 99%

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Oligodontia in the Clinical Spectrum of Syndromes: A Systematic Review

Castilho,

Resende,

Santos

et al. 2023

Preprint

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The aim of this systematic review was to describe clinical and genetic features of syndromes showing oligodontia as a sign. The review was performed according to the PRISMA 2020 checklist guidelines, and the search was conducted using PubMed, Scopus, Lilacs, Web of science, Livivo and EMBASE and supplemented by a gray literature search on Google Scholar and ProQuest, applying key terms relevant to the research questions. The systematic review identified 49 types of syndromes in 91 studies, and the most common was hypohidrotic ectodermal dysplasia, which was reported in 24 patients in 22 studies. Other commonest syndromes that reported oligodontia included Axenfeld-Rieger syndrome, Witkop’s syndrome, Ellis-van Creveld syndrome, blepharocheilodontic syndrome and oculo-facio-cardio-dental syndrome. The X-linked mode of inheritance was the most reported (n=14 studies), followed by the autosomal dominant (n=11 studies). The review describes the main syndromes that may have oligodontia as a clinical sign and reinforce the need of oro-dental-facial examining for adequate diagnosis and treatment of the affected patients. Molecular analysis in order to better understand the occurrence of oligodontia is imperative.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Oligodontia in the Clinical Spectrum of Syndromes: A Systematic Review

Castilho,

Resende,

Santos

et al. 2023

Preprint

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“…Mathematical morphology has been widely applied for edge detection. Multiscale morphology can be used to detect the edge of different directions, which basic elements including original data and structural elements [23]. The selection of structural elements directly affects the effect of image edge detection.…”

Section: Imsm_atb Algorithm 221 Improved Multiscale Morphologymentioning

confidence: 99%

Edge Detection of COVID-19 CT Image Based on GF_SSR, Improved Multiscale Morphology, and Adaptive Threshold

Hou¹,

Jia²,

Li³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Edge detection is an effective method for image segmentation and feature extraction. Therefore, extracting weak edges with the inhomogeneous gray of Corona Virus Disease 2019 (COVID-19) CT images is extremely important. Multiscale morphology has been widely used in the edge detection of medical images due to its excellent boundary detection accuracy. In this paper, we propose a weak edge detection method based on Gaussian filtering and singlescale Retinex (GF_SSR), and improved multiscale morphology and adaptive threshold binarization (IMSM_ATB). As all the CT images have noise, we propose to remove image noise by Gaussian filtering. The edge of CT images is enhanced using the SSR algorithm. In addition, based on the extracted edge of CT images using improved Multiscale morphology, a particle swarm optimization (PSO) algorithm is introduced to binarize the image by automatically getting the optimal threshold. To evaluate our method, we use images from three datasets, namely COVID-19, Kaggle-COVID-19, and COVID-Chestxray, respectively. The average values of results are worthy of reference, with the Shannon information entropy of 1.8539, the Precision of 0.9992, the Recall of 0.8224, the F-Score of 1.9158, running time of 11.3000. Finally, three types of lesion images in the COVID-19 dataset are selected to evaluate the visual effects of the proposed algorithm. Compared with the other four algorithms, the proposed algorithm effectively detects the weak edge of the lesion and provides help for image segmentation and feature extraction.

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“…An advantage of MTH is that it allows to process the image content from the most global to the most detailed level. Several works propose to improve different types of medical images by integrating MTH in a morphological based image enhancement approach [10,[17][18][19][20][21][22][23][24]. Currently, in the field of computer vision, MTH-based algorithms are used as a preliminary step for other applications based on artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

Dermoscopy Images Enhancement via Multi-Scale Morphological Operations

et al. 2021

Self Cite

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Skin dermoscopy images frequently lack contrast caused by varying light conditions. Indeed, often low contrast is seen in dermoscopy images of melanoma, causing the lesion to blend in with the surrounding skin. In addition, the low contrast prevents certain details from being seen in the image. Therefore, it is necessary to design an approach that can enhance the contrast and details of dermoscopic images. In this work, we propose a multi-scale morphological approach to reduce the impacts of lack of contrast and to enhance the quality of the images. By top-hat reconstruction, the local bright and dark features are extracted from the image. The local bright features are added and the dark features are subtracted from the image. In this way, images with higher contrast and detail are obtained. The proposed approach was applied to a database of 236 color images of benign and malignant melanocytic lesions. The results show that the multi-scale morphological approach by reconstruction is a competitive algorithm since it achieved a very satisfactory level of contrast enhancement and detail enhancement in dermoscopy images.

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Panoramic Dental Radiography Image Enhancement Using Multiscale Mathematical Morphology

Cited by 28 publications

References 44 publications

Oligodontia in the Clinical Spectrum of Syndromes: A Systematic Review

Oligodontia in the Clinical Spectrum of Syndromes: A Systematic Review

Edge Detection of COVID-19 CT Image Based on GF_SSR, Improved Multiscale Morphology, and Adaptive Threshold

Dermoscopy Images Enhancement via Multi-Scale Morphological Operations

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