A comparative study of MRI contrast enhancement techniques based on Traditional Gamma Correction and Adaptive Gamma Correction: Case of multiple sclerosis pathology

One of the most important preprocessing techniques is image Contrast Enhancement which is a technique that improve visual quality of an input image becoming more suitable for human analysis and perception. Numerous researches have been already developed for enhancement of Medical image for their important application, Traditional Gamma Correction is found to be one of the simplest technique for the contrast enhancement of medical image. This technique uses a set of varying parameter (y, c) which adjust effectively gray value intensity of the input image. Otherwise, Adaptive Gamma Correction technique have been appeared and have been proved its effectiveness by the use of adaptive (y, c) parameter which are determined adaptively from statistical information of input image. Traditional Gamma Correction (TGC) and Adaptive Gamma Correction (AGC) have been applied on three brain MRI modalities for patient with Multiple Sclerosis pathology. Qualitative and quantitative results are presented to illustrate the comparison of TGC and AGC to enhance the contrast of MRI images for better appearance of normal tissue and diseased tissue affected by MS pathology.

[1]  Manoj Kumar,et al.  Contrast Enhancement using Improved Adaptive Gamma Correction with Weighting Distribution Technique , 2014 .

[2]  Eunsung Lee,et al.  Contrast Enhancement Using Dominant Brightness Level Analysis and Adaptive Intensity Transformation for Remote Sensing Images , 2013, IEEE Geoscience and Remote Sensing Letters.

[3]  Ashish Kumar Bhandari,et al.  Improved normalised difference vegetation index method based on discrete cosine transform and singular value decomposition for satellite image processing , 2012, IET Signal Process..

[4]  Anil Kumar,et al.  Enhancement of Low Contrast Satellite Images using Discrete Cosine Transform and Singular Value Decomposition , 2011 .

[5]  A. Kouzani,et al.  Segmentation of multiple sclerosis lesions in MR images: a review , 2011, Neuroradiology.

[6]  Hiroaki Kotera,et al.  Dynamic range compression preserving local image contrast for digital video camera , 2005, IEEE Transactions on Consumer Electronics.

[7]  Mohammed Ghanbari,et al.  Low-contrast satellite images enhancement using discrete cosine transform pyramid and singular value decomposition , 2013, IET Image Process..

[8]  Ahmed Ben Hamida,et al.  A New Adaptive Gamma Correction Based Algorithm Using DWT-SVD for Non-Contrast CT Image Enhancement , 2017, IEEE Transactions on NanoBioscience.

[9]  N Senthilkumaran,et al.  A Study on Histogram Equalization for MRI Brain Image Enhancement , 2014 .

[10]  Gholamreza Anbarjafari,et al.  Satellite Image Contrast Enhancement Using Discrete Wavelet Transform and Singular Value Decomposition , 2010, IEEE Geoscience and Remote Sensing Letters.

[11]  Zhou Wang,et al.  Image Quality Assessment: From Error Measurement to Structural Similarity , 2004 .

[12]  Tianxu Zhang,et al.  Adaptive gamma correction based on cumulative histogram for enhancing near-infrared images , 2016 .

[13]  Ashish Kumar Bhandari,et al.  Dark satellite image enhancement using knee transfer function and gamma correction based on DWT–SVD , 2015, Multidimensional Systems and Signal Processing.

[14]  Joonki Paik,et al.  Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering , 1998 .

[15]  M W Weiner,et al.  A serial study of new MS lesions and the white matter from which they arise , 1998, Neurology.

[16]  H. Demirel,et al.  Image equalization based on singular value decomposition , 2008, 2008 23rd International Symposium on Computer and Information Sciences.

[17]  Shih-Chia Huang,et al.  Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution , 2013, IEEE Transactions on Image Processing.

[18]  Hsueh-Yen Yang,et al.  A Novel algorithm of local contrast enhancement for medical image , 2007, 2007 IEEE Nuclear Science Symposium Conference Record.

[19]  P. Shanmugavadivu,et al.  Particle swarm optimized multi-objective histogram equalization for image enhancement , 2014 .

[20]  Ashish Kumar Bhandari,et al.  Artificial Bee Colony-based satellite image contrast and brightness enhancement technique using DWT-SVD , 2014 .

[21]  Min Gyo Chung,et al.  Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement , 2008, IEEE Transactions on Consumer Electronics.

[22]  Li Yang,et al.  Image enhancement for liver CT images , 2009, International Conference on Optical Instruments and Technology.

[23]  P. Kalavathi,et al.  MEDICAL IMAGE CONTRAST ENHANCEMENT BASED ON GAMMA CORRECTION , 2012 .

[24]  C. Forbes Dewey,et al.  Biomedical Information Technology (PDF) , 2005 .

[25]  김정연,et al.  서브블록 히스토그램 등화기법을 이용한 개선된 콘트래스트 강화 알고리즘 ( An Advanced Contrast Enhancement Using Partially Overlapped Sub-Block Histogram Equalization ) , 1999 .

[26]  Qiong Song,et al.  High dynamic range infrared images detail enhancement based on local edge preserving filter , 2016 .

[27]  Antonio Miguel Cruz,et al.  Contrast enhancement with wavelet transform in radiological images , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).