A novel brain image enhancement method based on nonsubsampled contourlet transform

2017 ◽  
Vol 28 (2) ◽  
pp. 124-131 ◽  
Author(s):  
Liangliang Li ◽  
Yujuan Si ◽  
Zhenhong Jia
Keyword(s):  
Image Enhancement ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Brain Image ◽  
Enhancement Method

scholarly journals Retinal Vasculature Extraction using Non-Subsampled Contourlet Transform and Multi-structure Element Morphology by Reconstruction

2019 ◽  
Vol 8 (2) ◽  
pp. 116-125
Keyword(s):  
Image Enhancement ◽  
Quantitative Measure ◽  
Image Database ◽  
Contourlet Transform ◽  
Retinal Vasculature ◽  
Low Resolution ◽  
Average Accuracy ◽  
Enhancement Method ◽  
Boosting Technique ◽  
Normal Inverse Gaussian

Retinal vasculature extraction is an area of utmost interest in ophthalmology. It helps to diagnose various diseases and also play a crucial role in treatment planning and accomplishment.In this work, we suggest an algorithm to segmentretinal vasculature fromretinal Fundus Images(FI) using multi-structure element morphology after enhancing the image using Normal Inverse Gaussian (NIG) model in the fuzzified Non-Subsampled Contourlet Transform (NSCT) domain. Since both noises and weak edges produce low magnitude NSCT coefficients, image enhancement methods amplify weak edges as well as noises. Direct application of image boosting technique in the NSCT domain causes over enhancement. So a novel image enhancement method is employed by interpreting the term “contrast” as a qualitative instead of a quantitative measure of the image. Membership values of NSCT coefficients are modified using NIG model. Mathematical Morphology(MM) by Multi-structure Elements (MEs) is used to extract the edges of image. False vessel ridges are expunged, and the thin vessel edges are preserved using opening by reconstruction. Connected component analysis followed by length filtering is used to filter the still remaining false edges. In most of the available literature, low-resolution fundus image databases are used for evaluating the algorithm. In our work, we evaluate our algorithm not only utilizing the DRIVE database, a low-resolution retinal image (RI) database, but also using an openly available High-Resolution Fundus (HRF) image database. Our result illustrates that the proposed method outperforms the other techniques considered with average accuracy (ACC) of 96.71%. In addition to ACC, we also use F1-Score and Mathews Correlation Coefficient (MCC) to evaluate our method. The average values of the results obtained with the HRF image database for F1-Score and MCC are 0.8172 and 0.8031, respectively, which are very much encouraging

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.

A remote sensing image enhancement method using mean filter and unsharp masking in non-subsampled contourlet transform domain

2016 ◽  
Vol 39 (2) ◽  
pp. 183-193 ◽  
Author(s):  
Lu Liu ◽  
Zhenhong Jia ◽  
Jie Yang ◽  
Nikola Kasabov
Keyword(s):  
Remote Sensing ◽  
Image Enhancement ◽  
High Frequency ◽  
Low Frequency ◽  
Remote Sensing Image ◽  
Contourlet Transform ◽  
Inverse Transformation ◽  
Unsharp Masking ◽  
Mean Filter ◽  
Enhancement Method

The intelligibility of an image can be influenced by the pseudo-Gibbs phenomenon, a small dynamic range, low-contrast, blurred edge and noise pollution that occurs in the process of image enhancement. A new remote sensing image enhancement method using mean filter and unsharp masking methods based on non-subsampled contourlet transform (NSCT) in the scope for greyscale images is proposed in this paper. First, the initial image is decomposed into the NSCT domain with a low-frequency sub-band and several high-frequency sub-bands. Secondly, linear transformation is adopted for the coefficients of the low-frequency sub-band. The mean filter is used for the coefficients of the first high-frequency sub-band. Then, all sub-bands were reconstructed into spatial domains using the inverse transformation of NSCT. Finally, unsharp masking was used to enhance the details of the reconstructed image. The experimental results show that the proposed method is superior to other methods in improving image definition, image contrast and enhancing image edges.

Adaptive image enhancement using nonsubsampled contourlet transform domain histogram matching

2014 ◽  
Vol 12 (s2) ◽  
pp. S21002-321005
Author(s):  
Yan Zhou Yan Zhou ◽  
Qingwu Li Qingwu Li ◽  
Guanying Huo Guanying Huo
Keyword(s):  
Image Enhancement ◽  
Contourlet Transform ◽  
Transform Domain ◽  
Nonsubsampled Contourlet Transform ◽  
Histogram Matching

P3-345: MRI BRAIN IMAGE ENHANCEMENT USING CONTOURLET TRANSFORM ALONG WITH AN ENHANCEMENT FUNCTION

2006 ◽  
Vol 14 (7S_Part_23) ◽  
pp. P1216-P1216
Author(s):  
Ayesha Akter Lata ◽  
Inkyu Moon ◽  
Goo-Rak Kwon
Keyword(s):  
Image Enhancement ◽  
Contourlet Transform ◽  
Brain Image ◽  
Mri Brain ◽  
Mri Brain Image
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