Curvelet based Rayleigh CLAHE Medical Image Enhancement

Image enhancement is one of the main issues in digital image processing. Image enhancement is done to obtain a high quality image. This makes output image better than original image. Images that are obtained from medical imaging systems are of low quality. This may happen because available range of possible gray levels may not be utilized properly. Therefore images may suffer from underexposure and overexposure problems. A new algorithm has been proposed in this paper to enhance such medical images. A comparison of existing image enhancement techniques with the proposed technique based on different performance parameters is presented. Experimental results show that proposed technique is better than various existing techniques. General Terms Image Enhancement

[2]  Wang Jun,et al.  Combination of contrast limited adaptive histogram equalisation and discrete wavelet transform for image enhancement , 2015, IET Image Process..

[3]  Saurabh Maheshwari,et al.  Contrast limited adaptive histogram equalization based enhancement for real time video system , 2014, 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[5]  Ashish Verma,et al.  Contrast Enhancement of Medical Images-A Review , 2014 .

[6]  I. K. E. Purnama,et al.  COLOR ENHANCEMENT OF UNDERWATER CORAL REEF IMAGES USING CONTRAST LIMITED ADAPTIVE HISTOGRAM EQUALIZATION ( CLAHE ) WITH RAYLEIGH DISTRIBUTION , 2013 .

[7]  Rati Khandelwal,et al.  Various Image Enhancement Techniques - A Critical Review , 2014 .

[8]  Mehmet Celenk,et al.  An improved algorithm based on CLAHE for ultrasonic well logging image enhancement , 2018, Cluster Computing.

[9]  M. L. Dewal,et al.  Comparative Analysis of Curvelet Based Techniques for Denoising of Computed Tomography Images , 2011, 2011 International Conference on Devices and Communications (ICDeCom).

[10]  Azhari,et al.  Image contrast enhancement for film-based dental panoramic radiography , 2012, 2012 International Conference on System Engineering and Technology (ICSET).

[11]  Qi Guo,et al.  The study of medical image enhancement based on curvelet. , 2015, Technology and health care : official journal of the European Society for Engineering and Medicine.

[12]  Lee-Sup Kim,et al.  An advanced contrast enhancement using partially overlapped sub-block histogram equalization , 2001, IEEE Trans. Circuits Syst. Video Technol..

[13]  Sathit Intajag,et al.  Color retinal image enhancement by Rayleigh contrast-limited adaptive histogram equalization , 2014, 2014 14th International Conference on Control, Automation and Systems (ICCAS 2014).

[14]  Justin Joseph,et al.  An objective method to identify optimum clip-limit and histogram specification of contrast limited adaptive histogram equalization for MR images , 2017 .

[15]  Dong Kyun Lim,et al.  A Novel Method of Determining Parameters of CLAHE Based on Image Entropy , 2013 .

[16]  John D. Austin,et al.  Adaptive histogram equalization and its variations , 1987 .

[17]  J. Alex Stark,et al.  Adaptive image contrast enhancement using generalizations of histogram equalization , 2000, IEEE Trans. Image Process..

[18]  Savina Bansal,et al.  Comparative Study and Implementation of Image Processing Techniques Using MATLAB , 2012 .

[19]  S. Kang,et al.  Image Enhancement of Medical Images using Curvelet and Rayleigh CLAHE , 2018 .

[20]  Sargun Shashi B. Rana.  A Review of Medical Image Enhancement Techniques for Image Processing , 2011 .

[21]  Maryam Rastgarpour,et al.  An Image Enhancement Method Based on Curvelet Transform for CBCT-Images , 2017 .