scholarly journals MIE-NSCT: Adaptive MRI Enhancement Based on Nonsubsampled Contourlet Transform

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Caiwei Liu ◽  
Guohua Zhao ◽  
Jiale Dong ◽  
Yusong Lin ◽  
Meiyun Wang
Keyword(s):  
Medical Images ◽  
Disease Diagnosis ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
The Public ◽  
Low Pass ◽  
Magnetic Resonance Imaging Mri ◽  
Noise Amplification ◽  
Enhancement Algorithm

Image enhancement technology is often used to improve the quality of medical images and helps doctors or expert systems identify and diagnose diseases. This paper aimed at the characteristics of magnetic resonance imaging (MRI) with complex and difficult-to-enhance details and to propose a nonsubsampled contourlet transform- (NSCT-) based enhancement algorithm called MIE-NSCT. NSCT was used for MRI sub-band decomposition. For high-pass sub-bands, four fuzzy rules were proposed to enhance multiscale and multidirectional edge contour details from adjacent eight directions, whilst for low-pass sub-bands, a new adaptive histogram enhancement algorithm was proposed. The problem of noise amplification and loss of details during the enhancement process was solved. The algorithm was verified on the public dataset BraTS2017 and compared with other advanced methods. Experimental results showed that MIE-NSCT had obvious advantages in improving the quality of medical images, and high-quality medical images showed enhanced performance in grading tumour. MIE-NSCT is suitable for integration into an interactive expert system to provide support for the visualization of disease diagnosis.

Non-Subsampled Contourlet Transform-Based Effective Denoising of Medical Images

2019 ◽  
pp. 155-183
Author(s):  
Karthikeyan P. ◽  
Vasuki S. ◽  
Karthik K.
Keyword(s):  
Image Analysis ◽  
Edge Detection ◽  
Image Enhancement ◽  
Image Denoising ◽  
Medical Images ◽  
Noise Removal ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Shift Invariance ◽  
Image Edges

Noise removal in medical images remains a challenge for the researchers because noise removal introduces artifacts and blurring of the image. Developing medical image denoising algorithm is a difficult operation because a tradeoff between noise reduction and the preservation of actual features of image has to be made in a way that enhances and preserves the diagnostically relevant image content. A special member of the emerging family of multiscale geometric transforms is the contourlet transform which effectively captures the image edges and contours. This overcomes the limitations of the existing method of denoising using wavelet and curvelet. But due to down sampling and up sampling, the contourlet transform is shift-variant. However, shift-invariance is desirable in image analysis applications such as edge detection, contour characterization, and image enhancement. In this chapter, nonsubsampled contourlet transform (shift-invariance transform)-based denoising is presented which more effectively represents edges than contourlet transform.

scholarly journals An Image Enhancement Algorithm Based on Fractional-Order Phase Stretch Transform and Relative Total Variation

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wei Wang ◽  
Ying Jia ◽  
Qiming Wang ◽  
Pengfei Xu
Keyword(s):  
Image Enhancement ◽  
Total Variation ◽  
Fractional Order ◽  
Complex Environments ◽  
Low Contrast ◽  
Low Pass ◽  
Phase Stretch Transform ◽  
Enhancement Algorithm ◽  
Relative Total Variation

The main purpose of image enhancement technology is to improve the quality of the image to better assist those activities of daily life that are widely dependent on it like healthcare, industries, education, and surveillance. Due to the influence of complex environments, there are risks of insufficient detail and low contrast in some images. Existing enhancement algorithms are prone to overexposure and improper detail processing. This paper attempts to improve the treatment effect of Phase Stretch Transform (PST) on the information of low and medium frequencies. For this purpose, an image enhancement algorithm on the basis of fractional-order PST and relative total variation (FOPSTRTV) is developed to address the task. In this algorithm, the noise in the original image is removed by low-pass filtering, the edges of images are extracted by fractional-order PST, and then the images are fused with extracted edges through RTV. Finally, extensive experiments were used to verify the effect of the proposed algorithm with different datasets.

scholarly journals Classical and Fuzzy Based Image Enhancement Techniques for Banana Root Disease Diagnosis: A Review and Validation

2020 ◽  
Vol 13 (1) ◽  
pp. 50-62
Author(s):  
D. Suryaprabha ◽  
J. Satheeshkumar ◽  
N. Seenivasan
Keyword(s):  
Image Quality ◽  
Frequency Domain ◽  
Image Enhancement ◽  
Plant Root ◽  
Histogram Equalization ◽  
Disease Diagnosis ◽  
Root Disease ◽  
Classical Point ◽  
Enhancement Algorithm

A vital step in automation of plant root disease diagnosis is to extract root region from the input images in an automatic and consistent manner. However, performance of segmentation algorithm over root images directly depends on the quality of input images. During acquisition, the captured root images are distorted by numerous external factors like lighting conditions, dust and so on. Hence it is essential to incorporate an image enhancement algorithm as a pre-processing step in the plant root disease diagnosis module. Image quality can be improved either by manipulating the pixels through spatial or frequency domain. In spatial domain, images are directly manipulated using their pixel values and alternatively in frequency domain, images are indirectly manipulated using transformations. Spatial based enhancement methods are considered as favourable approach for real time root images as it is simple and easy to understand with low computational complexity. In this study, real time banana root images were enhanced by attempting with different spatial based image enhancement techniques. Different classical point processing methods (contrast stretching, logarithmic transformation, power law transformation, histogram equalization, adaptive histogram equalization and histogram matching) and fuzzy based enhancement methods using fuzzy intensification operator and fuzzy if-then rule based methods were tried to enhance the banana root images. Quality of the enhanced root images obtained through different classical point processing and fuzzy based methods were measured using no-reference image quality metrics, entropy and blind image quality index. Hence, this study concludes that fuzzy based method could be deployed as a suitable image enhancement algorithm while devising the image processing modules for banana root disease diagnosis.

scholarly journals Integrated Modelling Approach for Enhancing Brain MRI with Flexible Pre-Processing Capability

2019 ◽  
Vol 9 (4) ◽  
pp. 2416
Author(s):  
Harish S ◽  
G.F Ali Ahammed
Keyword(s):  
Information Quality ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Integrated Modelling ◽  
Discrete Problems ◽  
Magnetic Resonance Imaging Mri ◽  
Novel Model ◽  
Enhancement Algorithm ◽  
Modelling Approach

The assurance of an information quality of the input medical image is a critical step to offer highly precise and reliable diagnosis of clinical condition in human. The importance of such assurance becomes more while dealing with important organ like brain. Magnetic Resonance Imaging (MRI) is one of the most trusted mediums to investigate brain. Looking into the existing trends of investigating brain MRI, it was observed that researchers are more prone to investigate advanced problems e.g. segmentation, localization, classification, etc considering image dataset. There is less work carried out towards image preprocessing that potential affects the later stage of diagnosing. Therefore, this paper introduces a novel model of integrated image enhancement algorithm that is capable of solving different and discrete problems of performing image pre-processing for offering highly improved and enhanced brain MRI. The comparative outcomes exhibit the advantage of its simplistic implemetation strategy.

CONTRAST ENHANCEMENT USING TEXTURE HISTOGRAM AND FUZZY ENTROPY

2007 ◽  
Vol 03 (03) ◽  
pp. 349-365
Author(s):  
YANHUI GUO ◽  
H. D. CHENG ◽  
JIANHUA HUANG ◽  
WEI ZHAO ◽  
XIANGLONG TANG
Keyword(s):  
Contrast Enhancement ◽  
Image Enhancement ◽  
Fuzzy Entropy ◽  
Experimental Results ◽  
Noise Amplification ◽  
Enhancement Algorithm

Image enhancement is used to correct contrast deficiencies and to improve the quality of an image. It is essential and critical to extracting features and segmenting images. This paper presents a novel contrast enhancement algorithm based on newly defined texture histogram and fuzzy entropy with the ability to preserve edges and details, while avoiding noise amplification and over-enhancement. To demonstrate the performance, the proposed algorithm is tested on a variety of images and compared with other enhancement algorithms. Experimental results proved that the proposed method has better performance in enhancing images without over-enhancement and under-enhancement.

A Multi-View Sonar Image Fusion Method Based on Nonsubsampled Contourlet Transform and Morphological Modification

2014 ◽  
Vol 530-531 ◽  
pp. 567-570 ◽  
Author(s):  
Zhi Gang Zhang ◽  
Hong Yu Bian ◽  
Zi Qi Song ◽  
Hui Xu
Keyword(s):  
Image Fusion ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Multi Scale ◽  
Sonar Image ◽  
Morphological Modification ◽  
Morphology Modification ◽  
Sonar Images ◽  
Set Up

In underwater detection, a single image from the image sonar doesnt have the capacity to fully describe the surface of the interested target. In practice, a target is usually detected from various views, and similar contours and textures in a series of multi-view images can be employed. This work offers a novel technique for the fusion of a series of sonar images in multi-view detection of the same target, to improve the quality of images and repair the regions of target. The method takes advantage of Nonsubsampled Contourlet Transform (NSCT) to acquire coefficient matrixes of different resolutions and directions. Besides, a framework for sonar image fusion is set up based on morphology modification. The coefficient matrixes in NSCT domain are fused in the multi-scale framework and revised in decision-making. Experimental results show that target regions in the fused sonar images are effectively repaired and image quality also get improved evidently.

Medical images fusion of nonsubsampled Contourlet transform and regional feature

2013 ◽  
Vol 33 (6) ◽  
pp. 1727-1731 ◽  
Author(s):  
Chao LI ◽  
Guangyao LI ◽  
Yunlan TAN ◽  
Xianglong XU
Keyword(s):  
Medical Images ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Regional Feature

A Practical Medical Image Enhancement Algorithm Based on Nonsubsampled Contourlet Transform

2019 ◽  
Vol 9 (5) ◽  
pp. 1046-1056 ◽  
Author(s):  
Liangliang Li ◽  
Linli Wang ◽  
Zhenhong Jia ◽  
Yujuan Si ◽  
Jie Yang ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Medical Image ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Medical Image Enhancement ◽  
Enhancement Algorithm
Sign in / Sign up

Export Citation Format

Share Document