scholarly journals A Framework for Cross-Modality Guided Contrast Enhancement of CT Liver Using MRI

2021 ◽  
Vol 38 (6) ◽  
pp. 1671-1675
Author(s):  
Ahmed Elaraby ◽  
Ayman Taha
Keyword(s):  
Medical Imaging ◽  
Contrast Enhancement ◽  
Local Contrast ◽  
Ct Image ◽  
Histogram Modification ◽  
Low Contrast ◽  
Computed Tomographic ◽  
Novel Approach ◽  
Image Contrast Enhancement ◽  
Adaptive Gamma Correction

In liver medical imaging, physicians always detect, monitor, and characterize liver diseases by visually assessing of liver medical images. Computed Tomographic (CT) imaging is considered as one of the efficient medical imaging modalities in diagnosis of various human diseases. However, imprecise visualization and low contrast are the drawbacks that limit its utility. In this paper, a novel approach of multimodal liver image contrast enhancement is proposed. The idea behind the proposed approach is utilizing MRI scan as guide to exploit the diversity information extracted to enhance the structures in CT modal of liver. The proposed enhancement technique consists of two phases of enhancement to assess local contrast of the input images. In the first phase, the two image modalities are converted to the same range as the range of MRI and CT are different. Then, we did transformation of CT image so that its histogram matches the histogram of MRI. Second, the adaptive gamma correction-based histogram modification is utilized to get enhanced CT image. The subjective and objective experimental results indicated that the proposed scheme generates significantly enhanced liver CT.

scholarly journals Multipeak Mean Based Optimized Histogram Modification Framework Using Swarm Intelligence for Image Contrast Enhancement

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
P. Babu ◽  
V. Rajamani ◽  
K. Balasubramanian
Keyword(s):  
Contrast Enhancement ◽  
Mean Value ◽  
Input Image ◽  
Natural Image ◽  
Bicriteria Optimization ◽  
Histogram Modification ◽  
Multiple Peaks ◽  
Local Maxima ◽  
Novel Approach ◽  
Image Contrast Enhancement

A novel approach, Multipeak mean based optimized histogram modification framework (MMOHM) is introduced for the purpose of enhancing the contrast as well as preserving essential details for any given gray scale and colour images. The basic idea of this technique is the calculation of multiple peaks (local maxima) from the original histogram. The mean value of multiple peaks is computed and the input image’s histogram is segmented into two subhistograms based on this multipeak mean (mmean) value. Then, a bicriteria optimization problem is formulated and the subhistograms are modified by selecting optimal contrast enhancement parameters. While formulating the enhancement parameters, particle swarm optimization is employed to find optimal values of them. Finally, the union of the modified subhistograms produces a contrast enhanced and details preserved output image. This mechanism enhances the contrast of the input image better than the existing contemporary HE methods. The performance of the proposed method is well supported by the contrast enhancement quantitative metrics such as discrete entropy, natural image quality evaluator, and absolute mean brightness error.

scholarly journals Fast contrast enhancement by adaptive pixel value stretching

2018 ◽  
Vol 14 (8) ◽  
pp. 155014771879380
Author(s):  
Gang Cao ◽  
Huawei Tian ◽  
Lifang Yu ◽  
Xianglin Huang
Keyword(s):  
Contrast Enhancement ◽  
Dynamic Range ◽  
Computational Cost ◽  
Computer Applications ◽  
Visual Sensor ◽  
Visual Sensor Network ◽  
Image Contrast Enhancement ◽  
Quantitative Performance ◽  
Pixel Value ◽  
Adaptive Gamma Correction

In this article, we propose a fast and effective method for digital image contrast enhancement. The gray-level dynamic range of contrast-distorted images is extended maximally via adaptive pixel value stretching. The quantity of saturated pixels is set intelligently according to the perceptual brightness of global images. Adaptive gamma correction is also novelly used to recover the normal luminance in enhancing dimmed images. Different from prior methods, our proposed technique could be enforced automatically without complex manual parameter adjustment per image. Both qualitative and quantitative performance evaluation results show that, comparing with some recent influential contrast enhancement techniques, our proposed method achieves comparative or better enhancement quality at a surprisingly lower computational cost. Besides general computer applications, such merit should also be valuable in low-power scenarios, such as the imaging pipelines used in small mobile terminals and visual sensor network.

scholarly journals Reversible Data Hiding With Image Contrast Enhancement Based on Two-Dimensional Histogram Modification

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 83332-83342 ◽  
Author(s):  
Hao-Tian Wu ◽  
Weiqi Mai ◽  
Shuyi Meng ◽  
Yiu-Ming Cheung ◽  
Shaohua Tang
Keyword(s):  
Contrast Enhancement ◽  
Data Hiding ◽  
Reversible Data Hiding ◽  
Image Contrast ◽  
Two Dimensional ◽  
Histogram Modification ◽  
Image Contrast Enhancement

scholarly journals Local Contrast Enhancement Utilizing Bidirectional Switching Equalization of Separated and Clipped Subhistograms

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Haidi Ibrahim ◽  
Seng Chun Hoo
Keyword(s):  
Contrast Enhancement ◽  
Histogram Equalization ◽  
Processing Technique ◽  
Image Contrast ◽  
Local Contrast ◽  
Image Contrast Enhancement ◽  
Enhancement Method ◽  
Equalization Technique ◽  
Histogram Bins ◽  
Almost All

Digital image contrast enhancement methods that are based on histogram equalization technique are still useful for the use in consumer electronic products due to their simple implementation. However, almost all the suggested enhancement methods are using global processing technique, which does not emphasize local contents. Therefore, this paper proposes a new local image contrast enhancement method, based on histogram equalization technique, which not only enhances the contrast, but also increases the sharpness of the image. Besides, this method is also able to preserve the mean brightness of the image. In order to limit the noise amplification, this newly proposed method utilizes local mean-separation, and clipped histogram bins methodologies. Based on nine test color images and the benchmark with other three histogram equalization based methods, the proposed technique shows the best overall performance.

Memetic Algorithm Image Enhancement for Preserving Mean Brightness Without Losing Image Features

2019 ◽  
Vol 19 (04) ◽  
pp. 1950020
Author(s):  
Mitra Montazeri
Keyword(s):  
Contrast Enhancement ◽  
Dynamic Range ◽  
Memetic Algorithm ◽  
Image Features ◽  
Image Contrast ◽  
Consumer Electronics ◽  
Major Step ◽  
Processing Application ◽  
Low Contrast ◽  
Image Contrast Enhancement

In the image processing application, contrast enhancement is a major step. Conventional contrast enhancement methods such as Histogram Equalization (HE) do not have satisfactory results on many different low contrast images and they also cannot automatically handle different images. These problems result in specifying parameters manually to produce high contrast images. In this paper, an automatic image contrast enhancement on Memetic algorithm (MA) is proposed. In this study, simple exploiter is proposed to improve the current image contrast. The proposed method accomplishes multi goals of preserving brightness, retaining the shape features of the original histogram and controlling excessive enhancement rate, suiting for applications of consumer electronics. Simulation results shows that in terms of visual assessment, peak signal-to-noise (PSNR) and Absolute Mean Brightness Error (AMBE) the proposed method is better than the literature methods. It improves natural looking images specifically in images with high dynamic range and the output images were applicable for products of consumer electronic.

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