Generalized Histogram Equalization Based on Local Characteristics

Jen, Tzu-Cheng; Wang, Sheng-Jyh

doi:10.1109/icip.2006.313030

Cited by 12 publications

(6 citation statements)

References 6 publications

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“…This technique manipulates the black and white in the images, which may alter the perceived visual appearance of the images [4]. The global approach, termed a histogram equalization (HE), enhanced the original image through brightness intensity distribution applied to the whole image [6] [35], which makes the image become over-enhanced and look unnatural [36]. Due to this effect, the use of the adaptive histogram equalization (AHE) is introduced [6] [35][36].…”

Section: Introductionmentioning

confidence: 99%

“…The global approach, termed a histogram equalization (HE), enhanced the original image through brightness intensity distribution applied to the whole image [6] [35], which makes the image become over-enhanced and look unnatural [36]. Due to this effect, the use of the adaptive histogram equalization (AHE) is introduced [6] [35][36]. This approach overcomes the drawback of HE but produces a washout effect [37] and introduces artifacts [36].…”

Section: Introductionmentioning

confidence: 99%

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Correlation between Quantitative and Qualitative Analysis on Image Quality of Digital Dental X-ray Images

Ahmad¹,

Taib²,

Khalid³

et al. 2012

JCSCM

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Signal to Noise Ratio (SNR) is a quantitative method to measure the effectiveness of contrast enhancement methods. The question is whether this measurement is consistent with a qualitative evaluation of a medical expert. Biomedical Research, including dentistry cases, usually involves medical experts to do the interpretation, and finalize the diagnosis. However, the subjective evaluations create differences of interpretation. There are two factors contributing to the low quality of dental radiographs-the first is because of the nature of the image acquisition process, and the second is the low X-ray dose usage. Due to that, research related to contrast enhancement algorithms is an accepted image processing method to assist medical officers in doing interpretation that is more reliable. Thus, this work presents an analysis of contrast enhancement algorithms (CEA) applied to digital intraoral radiograph images. The enhancement methods used in this study are CEAs, namely: adaptive histogram equalization (AHE), contrast limited adaptive histogram equalization (CLAHE), and sharp contrast limited adaptive histogram equalization (SCLAHE). The objective of this work is to find a correlation between the dentist evaluation and SNR values in determining the image quality based on the rate of abnormalities detected. Fifty-six original intraoral digital dental X-ray images are collected from the Faculty of Dentistry, Universiti Teknologi MARA Malaysia. These images are processed with the mentioned CEAs, resulting in a total of onehundred sixty-six observations. These images are compared against the original images. The method of assessment is through questionnaires where all images are arranged randomly. The observations focused on identifying widened periodontal ligament space (widened PDLs), periapical radiolucency (PR) and loss of lamina dura (loss LD) abnormalities. These are among the main signs of periapical disease. The results show that the SNR value does not reflect the dentist's evaluation in terms of image quality. The SNR values of CLAHE are able to precede other methods. However, SCLAHE is better than the rest based on dentists' evaluations for widened PDLs abnormality.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Correlation between Quantitative and Qualitative Analysis on Image Quality of Digital Dental X-ray Images

Ahmad¹,

Taib²,

Khalid³

et al. 2012

JCSCM

View full text Add to dashboard Cite

show abstract

“…It enhanced the original image through brightness intensity values redistribution of the whole image [6]. However the approach makes the output image gets over-enhanced and the look unnatural [7]. Due to this disadvantage AHE is introduced.…”

Section: Introductionmentioning

confidence: 99%

“…CLAHE is a type of local contrast-gain limited by restricting the height of local histogram [8]. This method had been successfully applied to mammograms [7], retinal images [9] and bone fracture crack x-ray detection [10]. Yet, CLAHE disadvantage is that the amount of enhancement for the foreground and background are alike.…”

Section: Introductionmentioning

confidence: 99%

The qualitative and quantitative evaluation of enhanced dental radiographs

Ahmad

Taib

Khalid

et al. 2011

International Conference on Electrical, Control and Computer Engineering 2011 (InECCE)

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Qualitative and quantitative evaluation is important in verifying the effectiveness in any enhancement techniques. The contrast enhancement of dental radiographs is common due to the nature of the intra-oral radiographs that sometimes suffer noises and lack of sufficient brightness. This work compares the qualitative evaluation of dentists to quantitative measurement of statistical values of contrast improvement index (CII), signal to noise ratio (SNR) and root mean square error (RMSE) on intra-oral dental x-ray images before and after processing with sharp adaptive histogram equalization (SAHE) and sharp contrast-limited adaptive histogram equalization (SCLAHE). Results show that there are consistencies in the dentists' evaluation and the statistical values obtained. As for the techniques it shows that SCLAHE have the positive effect on providing better information for dentists.Keywords-dental x-ray image; periapical lesion; image processing.I.

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“…Though, AHE drawbacks was that its operation resultant wash out effect [24], introduces artifacts [25] and losing out the image details [26]. Consequently, the Contrast Limit Histogram Equalization (CLAHE) is created by limiting the local contrast-gain by restricting the height of local histogram [12].…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Image Enhancement Techniques for Dental X-ray Image Interpretation

Ahmad¹,

Taib²,

Khalid³

et al. 2012

IJMLC

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Abstract-This paper presents qualitative and quantitative comparison between original images and four image enhancement techniques namely adaptive histogram equalization (AHE), contrast adaptive histogram equalization (CLAHE), median adaptive histogram equalization (MAHE) and sharp contrast adaptive histogram equalization (SCLAHE) applied to dental x-ray images. Dental x-ray images usually taken with low radiation dosage are often presented as dark, low in contrast and noisy. These problems are usually solved with image enhancement techniques. However, choosing an appropriate technique is not an easy task especially for the purpose of disease diagnosis of periapical related lesion. This research involves the collection of ten intra-oral dental x-ray images which were collected from the Faculty of Dentistry UiTM Shah Alam, Malaysia. Each of the enhancement methods is applied to every collected image. A dentist was then asked to do the evaluation using questionnaire. She graded the quality of the images and the diagnostic ability of the periapical pathology observed in the images. Subsequently the quantitative analysis using contrast improvement index (CII), signal to noise ratio (SNR) and root mean square error (RMSE) were done to investigate the characteristic of the images base on the dentist evaluation. The finding shows that the enhancement techniques managed to enhance the pathology slightly better than the original image.Index Terms-Contrast enhancement, adaptive histogram equalization (AHE), dental X-ray.

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Generalized Histogram Equalization Based on Local Characteristics

Cited by 12 publications

References 6 publications

Correlation between Quantitative and Qualitative Analysis on Image Quality of Digital Dental X-ray Images

Correlation between Quantitative and Qualitative Analysis on Image Quality of Digital Dental X-ray Images

The qualitative and quantitative evaluation of enhanced dental radiographs

An Analysis of Image Enhancement Techniques for Dental X-ray Image Interpretation

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