Book Chapter10.1007/978-981-15-0947-6_63
Adaptive Local Gray-Level Transformation Based on Variable S-Curve for Contrast Enhancement of Mammogram Images
Hamid El malali,Abdelhadi Assir,Mohammed Harmouchi,Mourad Rattal,Aissam Lyazidi,Azeddine Mouhsen +5 more
- 01 Jan 2020
- pp 671-679
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TL;DR: An adaptive local gray-level s-curve transformation is proposed to improve as much contrast of mammogram images as possible and is compared with three existing techniques based on histogram equalization.
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Abstract: Mammogram image enhancement plays an important role in the accuracy of the diagnosis. Resulting images must allow a good contrast and better appearance of the limiting edges between different areas in the image, while preserving both information and brightness. In this paper, an adaptive local gray-level s-curve transformation is proposed. The main aim is to improve as much contrast of mammogram images as possible. The principle is to find all the parameters of the local gray-level transformation for each image which will allow for a better improvement using the genetic algorithm that is among global optimization methods. The evaluation of the results found is done based on image quality assessment (IQA) metrics and visual inspection in comparison with three existing techniques based on histogram equalization.
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Citations
Efficient Contrast Adjustment and Fusion Method for Underexposed Images in Industrial Cyber-Physical Systems
Zia-Uu Rahman,Muhammad Aamir,Zafar Ali,Abdul Khader Jilani Saudagar,Abdullah Altameem,Khan Muhammad +5 more
TL;DR: In this article , an effective visibility enhancement model is proposed to eliminate inconsistent color casts while highlighting more hidden content for improved inspection, safety in large spaces, and monitoring of large systems.
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A Contrast Enhancement Model for X-Ray Mammograms Using Modified Local s-Curve Transformation Based on Multi-Objective Optimization
TL;DR: The multi-objective optimization problem is transformed into a single- objective optimization to find a unique solution for mammogram enhancement.
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Diagnosis of Breast Cancer by K-Mean Clustering and Otsu Thresholding Segmentation Methods
Aslı Kuşçu,Halil Erol +1 more
TL;DR: In this article , K-mean clustering and Otsu threshold method were used to detect 9 cancerous images taken from the TCİA image data bank were detected by K-means clustering.
Fully Automatic Computer-Aided Detection of Breast Cancer based on Genetic Algorithm Optimization
Hamid El malali,Abdelhadi Assir,Mohammed Harmouchi,Aissam Lyazidi,Mourad Rattal,Azeddine Mouhsen +5 more
- 16 Apr 2020
TL;DR: A full automatic CADe is presented to assist radiologists to make the right decision by showing them the probably suspect area and both contrast enhancement method and the segmentation method are performed by genetic algorithm to optimize the outcomes of each step.
References
FSIM: A Feature Similarity Index for Image Quality Assessment
TL;DR: A novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features.
IEM: A New Image Enhancement Metric for Contrast and Sharpness Measurements
Jaya V. L,R. Gopikakumari +1 more
TL;DR: A new metric is proposed that is useful for measuring the improvement in contrast as well as sharpness of both general and medical images and can be used for all types of images.
Mammographic Image Enhancement Using Indirect Contrast Enhancement Techniques – A Comparative Study☆
TL;DR: Few indirect contrast enhancement techniques namely histogram equalization, CLAHE, BBHE, RMSHE, MMBEBHE to preprocess the mammographic images are applied and the performance is measured using effective measure of enhancement (EME) and peak signal to noise ratio (PSNR).
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•Journal Article
Efficient Algorithm for Contrast Enhancement of Natural Images
Shyam Lal,Mahesh Chandra +1 more
TL;DR: Simulation and experimental results on benchmark test images demonstrates that proposed algorithm provides better results as compared to other state*of*art contrast enhancement techniques.
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Human visual system based unsharp masking for enhancement of mammographic images
TL;DR: The usage of NPF in design of Non-Linear Unsharp Masking (UM) framework for the enhancement of X-ray mammograms (digital mammographic images) is presented and it has been subjectively as well as objectively shown that the Enhancement of the contrast and edges do not introduces unwanted overshoots in the ROI.
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