Teeth Segmentation Using Gamma Adjustment and Transition Region Filter Based on Wavelet Method

Arifin, Agus Zainal; Syuhada, Fahmi; Ni'mah, Ana Tsalitsatun; Suryaningrum, Dwi Ari; Indraswari, Rarasmaya; Navastara, Dini Adni

doi:10.1109/icic47613.2019.8985725

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Ravi Kumar et al (6) introduced an adaptive weighted distribution-oriented gamma correction method specifically designed for brightness correction and modification of MRI images. In a separate study, Agus Zainal Arifin et al (7) utilized gamma correction for teeth image segmentation. They conducted experiments with gamma values ranging from 1 to 10.…”

Section: Introductionmentioning

confidence: 99%

Brain MRI/CT Contrast Enhancement using Hybrid Transformation

Halder,

Sarkar,

Mandal

et al. 2023

IJST

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Objectives: To develop a better method for enhancing head MRI or CT images with the objective of suppressing irrelevant information during subsequent processing. Methods: To achieve this, two innovative image enhancement techniques, 'AHE' and 'Adaptive Gamma with CDF-based Geometric Transformation,' are combined. The proposed method is evaluated using a total of 76 MRI and 137 CT scan images obtained from the TCIA, OASIS, BCIIHM, and Brain metastases datasets. To tailor these methods for head MRI and CT images, two background masking algorithms have been introduced in this regard. Furthermore, the dark, grey, and white segments of the histogram are identified and geometrically transformed into square areas, followed by a gamma transformation applied to each transformed segment. Finally, a combination of global gamma transformation and AHE is applied to achieve the final enhancement. The proposed method is compared with other state-of-the-art techniques based on evaluation parameters such as "contrast, "correlation, and "entropy. Findings: Both qualitative and quantitative analysis exhibit how the proposed method has better enhancement capability than the CLAHE, AGC, and EGC methods. Our approach yields the highest average contrast, correlation, and entropy values for MRI, measuring 0.522319529, 0.957321634, and 5.585280467, respectively. For CT images, the suggested approach produces the maximum entropy value, 3.472600537, and the average contrast, 0.151943828, which is just less than CLAHE. The average correlation for CT, 0.98799312, is also a hair less than EGC. These results suggest that the proposed method could potentially make the details and structures in the MRI and CT more distinguishable. Novelty : The suggested approach uses a 2D geometric transformation for enhancing contrast of brain CT/MRI images and two robust head masking processes for removing noise in the background. The comparative analysis shows that the proposed method exhibits superior contrast enhancement capability compared to the other three methods being compared.

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Section: Introductionmentioning

confidence: 99%

Brain MRI/CT Contrast Enhancement using Hybrid Transformation

Halder,

Sarkar,

Mandal

et al. 2023

IJST

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Tooth Localization using YOLOv3 for Dental Diagnosis on Panoramic Radiographs

2022

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Detection of Logos of Moving Vehicles under Complex Lighting Conditions

Zhao

Guo

2022

Applied Sciences

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This study proposes a method for vehicle logo detection and recognition to detect missing and inaccurate vehicle marks under complex lighting conditions. For images acquired in complex light conditions, adaptive image enhancement is used to improve the accuracy of car sign detection by more than 2%; for the problems of multi-scale and detection speed of vehicle logo recognition in different images, the paper improves the target detection algorithm to improve the detection accuracy by more than 3%. The adaptive image enhancement algorithm and improved You Only Look One-level Feature (YOLOF) detection algorithm proposed in this study can effectively improve the correct identification rate under complex lighting conditions.

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Teeth Segmentation Using Gamma Adjustment and Transition Region Filter Based on Wavelet Method

Cited by 3 publications

References 17 publications

Brain MRI/CT Contrast Enhancement using Hybrid Transformation

Brain MRI/CT Contrast Enhancement using Hybrid Transformation

Tooth Localization using YOLOv3 for Dental Diagnosis on Panoramic Radiographs

Detection of Logos of Moving Vehicles under Complex Lighting Conditions

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