Image contrast enhancement for film-based dental panoramic radiography

As the panoramic x-ray is the most common extraoral radiography in dentistry, segmentation of its anatomical structures facilitates diagnosis and registration of dental records. This study presents a fast and accurate method for automatic segmentation of mandible in panoramic x-rays. In the proposed four-step algorithm, a superior border is extracted through horizontal integral projections. A modified Canny edge detector accompanied by morphological operators extracts the inferior border of the mandible body. The exterior borders of ramuses are extracted through a contour tracing method based on the average model of mandible. The best-matched template is fetched from the atlas of mandibles to complete the contour of left and right processes. The algorithm was tested on a set of 95 panoramic x-rays. Evaluating the results against manual segmentations of three expert dentists showed that the method is robust. It achieved an average performance of [Formula: see text] in Dice similarity, specificity, and sensitivity.

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“…CLAHE emphasizes the edges in each tile but does not intensify the noise in homogenous areas by putting a limit on the contrast increase. 17…”

Section: Proposed Methodsmentioning

confidence: 99%

Automatic segmentation of mandible in panoramic x-ray

2015

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“…Kim et al (2001) described that histogram equalization was a simple and effective image enhancement technique for enhancing the contrast of digital images. In this, pixels were assumed to be uniformly distributed over the entire image [7]. Bhadauria et al (2011) explained curvelet based noise reduction methods.…”

Section: Introductionmentioning

confidence: 99%

“…The experimental results showed that curvelet based methods outperformed wavelet based methods [8]. Suprijanto et al (2012) discussed different image enhancement techniques like contrast stretching, HE, AHE, CLAHE Rayleigh. These techniques were processed on dental imaging.…”

Section: Introductionmentioning

confidence: 99%

Curvelet based Rayleigh CLAHE Medical Image Enhancement

Gupta¹,

Singh²

2018

IJCA

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Image enhancement is one of the main issues in digital image processing. Image enhancement is done to obtain a high quality image. This makes output image better than original image. Images that are obtained from medical imaging systems are of low quality. This may happen because available range of possible gray levels may not be utilized properly. Therefore images may suffer from underexposure and overexposure problems. A new algorithm has been proposed in this paper to enhance such medical images. A comparison of existing image enhancement techniques with the proposed technique based on different performance parameters is presented. Experimental results show that proposed technique is better than various existing techniques. General TermsImage Enhancement

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“…They deal with pixels of image directly. Researchers have made great efforts to spatial techniques and have made important results about image enhancement [2][3][4]. Many denoising methods are processed in transform domain.…”

Section: Introductionmentioning

confidence: 99%

A novel method of image enhancement based on the fractional Fourier transform

2014

2014 7th International Congress on Image and Signal Processing

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A novel image enhancement method is presented in this paper. We apply the idea of Laplacian Pyramid and generalize it into the fractional Fourier domain to extract some image information. Firstly, we deal with the image in the fractional Fourier domain with a low-pass filter, and then the first level result can be obtained by subtracting the filtered image from the original image. The second level image thus can be received when applying the similar process to the low-pass filtered image of level 1. Further difference images can be achieved by iterating this process. Since the fractional Fourier transform has a rotation angle a, characters of image thus change in different transform domain. Image information may appear differently in every level of the difference images under some rotation angle, which makes it possible to extract image features more effectively in the fractional Fourier domain than that in the traditional Fourier domain. The proposed method provides an alternative way to enhance image with proper filter designing in the fractional Fourier transform. Simulations are provided to show the efficiency of this approach.

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Image contrast enhancement for film-based dental panoramic radiography

Cited by 11 publications

References 3 publications

Automatic segmentation of mandible in panoramic x-ray

Automatic segmentation of mandible in panoramic x-ray

Curvelet based Rayleigh CLAHE Medical Image Enhancement

A novel method of image enhancement based on the fractional Fourier transform

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