Enhancement of Dental Digital X-Ray Images based On the Image Quality

Menon, Hema P; Rajeshwari, B

doi:10.1007/978-3-319-47952-1_3

Cited by 7 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Adaptive local contrast stretching makes use of local homogeneity to solve the problem of over and under enhancement. One of the prominent methods to refine the contrast of the image is histogram equalization (HE) ( Harandi & Pourghassem, 2011 ; Menon & Rajeshwari, 2016 ; Obuchowicz Rafałand Nurzynska et al, 2018 ; Banday & Mir, 2019 ). HE is the way of extending the dynamic range of an image histogram and it also causes unrealistic impacts in images; however, it is very effective for scientific pictures i.e., satellite images, computed tomography, or X-rays.…”

Section: Methodsmentioning

confidence: 99%

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

Section: Methodsmentioning

confidence: 99%

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

show abstract

“…The quality of the digital dental X-rays depends on various factors, such as the sensors, lighting, noise and the captured area [20]. Those factors cause low contrast images that reduces the image quality and inhibit the extracting its useful information process.…”

Section: Contrast Limited Adaptive Histogram Equalization (Clahe)mentioning

confidence: 99%

“…Those factors cause low contrast images that reduces the image quality and inhibit the extracting its useful information process. The denoising filters can reduce noise but degrade the image contrast [20]. Therefore, improving the gray levels of the image in the spatial domain becomes popular methods.…”

Section: Contrast Limited Adaptive Histogram Equalization (Clahe)mentioning

confidence: 99%

“…Therefore, improving the gray levels of the image in the spatial domain becomes popular methods. The CLAHE is a general contrast enhancement technique that is widely used due to its ease and speed [20][21][22]. The CLAHE technique and gamma adjustment efficiently increase low contrast in medical imaging, including dental X-rays [22].…”

Section: Contrast Limited Adaptive Histogram Equalization (Clahe)mentioning

confidence: 99%

See 1 more Smart Citation

Segmenting Tooth Components in Dental X-Ray Images Using Gaussian Kernel- Based Conditional Spatial Fuzzy C-Means Clustering Algorithm

Fariza¹,

Arifin²,

Astuti³

et al. 2019

IJIES

View full text Add to dashboard Cite

Tooth component segmentation is a crucial task in computer-aided design for forensic odontology, especially to estimate chronological age. Tooth segmentation on radiographic data is a very challenging task due to noise, low contrast, and uneven illumination. The Fuzzy C-Means clustering is generally used for image segmentation that allow pixels to be classified into one or more clusters according to their membership value. However, this clustering method still has problems associated with the shifting of cluster centers and sensitivity to the overlapping intensity distributions between classes. This paper proposes a modified strategy of the conditional spatial Fuzzy C-Means (csFCM) that incorporates global and spatial information into a weighted membership function by replacing the Euclidean distance with the Gaussian kernel distance to increase insensitivity to noise and outliers. The aim of this paper is to divide the tooth into 3 components, i.e. enamel, dentine, and pulp. Therefore, this modified algorithm is preceded by dental X-ray image pre-processing and continued by combining each dental component clusters into one composite image. The tooth image is pre-processed using Contrast Limited Adequate Histogram Equalization (CLAHE) and gamma adjustment to enhance the dental X-ray images quality from the non-uniform lighting. The Gausian kernel-based conditional spatial Fuzzy C-Means (GK-csFCM) segments the dental image into four class clusters, namely enamel, dentine, pulp, and background. Through iterations, the resulting cluster centers are more convergent with real cluster centers, thus ensuring the proposed method improves the drawback of inherent FCM-based methods and further promoting image segmentation performance. The experimental results on the real dental X-ray images showed that GK-csFCM has better performance than the typical FCM and csFCM clustering algorithms in terms of both qualitative and quantitative metrics, i.e. accuracy, specificity, sensitivity, and precision.

show abstract

“…The gamma correction method (GCM) is applicable in such cases [10]. The GCM of image enhancement for dental images is evaluated in the proposed framework [11]. Another popular method is histogram-based histogram equalization and contrast limited adaptive histogram equalization (CLAHE) [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Evaluating spatial and frequency domain enhancement techniques on dental images to assist dental implant therapy

Shashikala¹,

Thangadurai

2021

IJECE

View full text Add to dashboard Cite

<span lang="EN-US">Dental imaging provides the patient's anatomical details for the dental implant based on the maxillofacial structure and the two-dimensional geometric projection, helping clinical experts decide whether the implant surgery is suitable for a particular patient. Dental images often suffer from problems associated with random noise and low contrast factors, which need effective preprocessing operations. However, each enhancement technique comes with some advantages and limitations. Therefore, choosing a suitable image enhancement method always a difficult task. In this paper, a universal framework is proposed that integrates the functionality of various enhancement mechanisms so that dentists can select a suitable method of their own choice to improve the quality of dental image for the implant procedure. The proposed framework evaluates the effectiveness of both frequency domain enhancement and spatial domain enhancement techniques on dental images. The selection of the best enhancement method further depends on the output image perceptibility responses, peak signal-to-noise ratio (PSNR), and sharpness. The proposed framework offers a flexible and scalable approach to the dental expert to perform enhancement of a dental image according to visual image features and different enhancement requirements.</span>

show abstract

Enhancement of Dental Digital X-Ray Images based On the Image Quality

Cited by 7 publications

References 14 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

Segmenting Tooth Components in Dental X-Ray Images Using Gaussian Kernel- Based Conditional Spatial Fuzzy C-Means Clustering Algorithm

Evaluating spatial and frequency domain enhancement techniques on dental images to assist dental implant therapy

Contact Info

Product

Resources

About