Indian Journal of Innovations and Developments

Open Access Open Access  Restricted Access Subscription Access

Advanced Medical Image Compression with 2-D Maximum Entropy Method and Hybrid Compression Concepts


Affiliations
1 Bharathiyar University, Coimbatore, India
 

Objective: In medical image processing, storage and transmission of image pixels is a vital problem since diagnosis is a continuous process.

Methods: The 2D maximum entropy method segmentation followed by compression provides good results in the field of Medical image processing which has storage and transmission problems.The proposed algorithm is created using 2-D maximum entropy method segmentation and followed by Hybrid image compression.This proposal introduces an advanced compression method which combines both lossy and lossless image compression techniques. The clinical part of the image is segmented with 2-D maximum entropy thresholding and lossless techniques are applied to compress this part. Lossy image compression technology is implemented in other part of the image that is with the background picture. Run Length Encoding is applied to the resultant data to produce the compressed image.

Findings: The Advantage of using 2-D Maximum entropy method is that it considers both the grey information and neighbouring information. It can be able to produce good results even the image’s signal to noise ratio (SNR) is low. Among lossy and lossless image compression methods lossless method is preferred in medical image processing since it has to protect important clinical information. Lossless compression can be able to produce lower compression ratio than lossy because it reduces the size of image only to certain limit. The experimental result shows that the proposed method provides good compression ratio and PSNR measures.

Novelty/Improvements:Future enhancement of this algorithm is that the neural network algorithm such as self-organizing feature map can be used to find out the threshold value automatically.


Keywords

Segmentation, 2-D Maximum Entropy Thresholding, Losslessimage Compression, Lossy Image Compression, Decompression.
User
Notifications

  • Due feng, Shi Wenkang. Infra red image segmentation with two dimensional maximum entropy based on particle swarm optimization. Pattern Recognition Letters.2005; 26 (5), 597-603.

  • Ghadah AJ-Khafaji, Wavelet transforms and polynomial approximation model for lossless medical image compression.International Journal of Advanced Research in Computer Sciecne and Software Engineering. 2014; 4(3), 1-4.

  • J Jiang. P. Trundle, J. Ren. Medical image analysis with artificial neural networks.Computerized Medical Imaging and Graphics. 2010; 34(8), 617-631.

  • Nikita Bansal, Sanjay Kumar Dubey. Image compression using hybrid transform technique.Journal of Global Research in Computer Science.2013;4(1), 13-17.

  • Liping Zheng, Guangvao Li. Improvement of grayscale image 2-D maximum entropy threshold segmentation method. IEEE, 2010; 324-328.

  • Manjinder Kaur,Gaganpreet Kaur. Survey of lossless and lossy image compression techniques.International Journal of Advanced Research in Computer Science and Software Engineering.2013; 3(2), 323-326.

  • Mussarat Yasmin, Muhammad Sharif and Sajjad Mohsin. Neural networks in medical imaging applications: A survey.World Applied Sciences Journal. 2013; 22(1), 85-96.

  • A.J.Rajeswari Joe, Dr.N.Rama. Neural network based image compression for memory consumption in cloud environment.Indian Journal of Science and Technology.2015; 8(15), 1-6.

  • Malvika Dixit. An enhanced hybrid technology for digital image compression.International Journal of Electronics and Communication Engineering.SSRG-IJECE.2014; 1(7), 21-27.

  • P.Daniel Ratna Raju, G.Neelima. Image Segmentation by using Histogram Thresholding.International Journal of computerscience Engineering and Technology.2012; 2(1), 776-779.

  • Neelesh Kumar Sahu. A survey on various medical image compression techniques.International Journal of Science, Engineering and Technology Research (IJSETR), 2013; 2(2), 501-506.

  • Sherif.e.hussein, Sheriff M.Badr. Healthcare cloud integration using distributed cloud storage and hybrid image compression. Indian Journal of Computer Applications. 2013; 80(3), 9-15.

  • S.Tajne, Amit, Prof. Pravin S. Kulkarni. A survey on medical image compression using Hybrid technique. International Journal of Computer Science and Mobile Computing. 2015; 4(2),18-23.

  • Serge Belongie,Jitendra Malik. Contour and texture analysis for Image segmentation.International Journal of Computer Vision.2001; 43(1),7-27.

  • J.Saira Banu, Rajasekhara Babu, Reeta Pandey. Parallel implementation of singular value decomposition(SVD) in image compression using open MP and sparse matrix pepresentation. Indian Journal of Science and Technology.2015; 8(13), 1-10.


Abstract Views: 240

PDF Views: 0




  • Advanced Medical Image Compression with 2-D Maximum Entropy Method and Hybrid Compression Concepts

Abstract Views: 240  |  PDF Views: 0

Authors

A. J. Rajeswari Joe
Bharathiyar University, Coimbatore, India
N. Rama
Bharathiyar University, Coimbatore, India

Abstract


Objective: In medical image processing, storage and transmission of image pixels is a vital problem since diagnosis is a continuous process.

Methods: The 2D maximum entropy method segmentation followed by compression provides good results in the field of Medical image processing which has storage and transmission problems.The proposed algorithm is created using 2-D maximum entropy method segmentation and followed by Hybrid image compression.This proposal introduces an advanced compression method which combines both lossy and lossless image compression techniques. The clinical part of the image is segmented with 2-D maximum entropy thresholding and lossless techniques are applied to compress this part. Lossy image compression technology is implemented in other part of the image that is with the background picture. Run Length Encoding is applied to the resultant data to produce the compressed image.

Findings: The Advantage of using 2-D Maximum entropy method is that it considers both the grey information and neighbouring information. It can be able to produce good results even the image’s signal to noise ratio (SNR) is low. Among lossy and lossless image compression methods lossless method is preferred in medical image processing since it has to protect important clinical information. Lossless compression can be able to produce lower compression ratio than lossy because it reduces the size of image only to certain limit. The experimental result shows that the proposed method provides good compression ratio and PSNR measures.

Novelty/Improvements:Future enhancement of this algorithm is that the neural network algorithm such as self-organizing feature map can be used to find out the threshold value automatically.


Keywords


Segmentation, 2-D Maximum Entropy Thresholding, Losslessimage Compression, Lossy Image Compression, Decompression.

References