Lossless Hybrid Coding technique based on Quasi Fractal & Oscillation Concept Method for Medical Image Compression

The Image compression is the most important entity in various fields. Image compression plays vital role in many applications. Out of which biomedical is one of the challenging applications. In medical research, everyday there is fast development and advancement. Medical researchers are thinking about digital storage of data hence medical image compression has a crucial role in hospitals. Here Morphological filter & adaptive threshold are used for refinement and used Quasi Fractal & Oscillation concept for developing new hybrid algorithm. Oscillation concept is lossy image compression technique hence applied on NonROI. Quasi fractal is lossless image compression technique applied on ROI. The experimental results shows that better CR with acceptable PSNR has been achieved using hybrid technique based on Morphological band pass filter and Adaptive thresholding for ROI. Here, innovative hybrid technique gives the CR 24.61 which improves a lot than hybrid method using BTCSPIHT is 5.65. Especially PSNR is also retained and bit improved i.e. 33.51. This hybrid technique gives better quality of an image.


Introduction
Image compression plays vital role in many applications. Out of which biomedical is one of the challenging application. In medical research, everyday there is fast development and advancement. Medical researchers are thinking about digital storage of data hence medical image compression has a crucial role in hospitals. Everyday high amount of medical images are captured in the medical laboratories. Due to this high amount of data, there is big question to store it in less space. Storing the data in less space becomes challenging to practitioners. Compression algorithms are developed by the researchers but compression of an image with high CR by keeping excellent image quality is a big challenge. Moreover to avoid diagnostic errors and reduce transmission time is also challenging. Image compression has immense importance as time consumption is very high to process out the original biomedical image. By using image compression algorithms we can compress different biomedical images. To reduce processing time required to retrieve target components from biomedical images, compression of image is necessary. In order to compress images different techniques are used i.e. lossy and lossless. To minimize the file size for storage PACS are used, which helps to keep information maintained. Compression algorithms in medical imaging has become challenging. For image compression many algorithms are used for achieving better CR & PSNR. In the field of medical there is a challenge to store more data hence image compression is one of the better solutions. Image compression is the scientific art of reducing size which is required for representing an image. These are useful & successful technologies in DIP field. Daily incalculable number of images is compressed & decompressed. Many images from web pages & high resolution cameras are also compressed. Especially it is used to get best accuracy of an image with a high CR. This research aims to improve the CR by keeping good quality of the image. Now a days various image compression techniques are used by researchers. DCT, DFT, DWT & Fractal are most commonly used existing image compression techniques. In order to achieve better results than these existing techniques we have implemented oscillation concept method. For compression, we can use MRI, CT & PET image. Both MRI and CT scan are used for detailing of brain images. MRI or CT images are used for Image compression which is effectively used for analysis of brain images. Here we have selected brain image for research. MRI is most effective in brain images hence we have selected MRI brain image for research. This assures applicability of lossless fractal image compression in medical field. Application of fractal compression to medical images would allow much higher CR with good picture quality. Quasi lossless fractal coding has been used for development of hybrid technique [1,2].

Oscillation Concept
Oscillation concept is new approach in image compression. This method explains theory of oscillations in images. Each and every image having vibrations and variations in it. These variations are known as oscillations. These variations are mainly in the pixel of an image. These variations are produced with respect to x & y axis. For compression of an image these oscillations are used. This method introduced for improving CR.
There is variation in grey scale intensities, these are oscillations in an image. This concept is utilized to find out the variations in biomedical images, appropriate oscillations are considered for image compression.
By repeating the process we can obtain the Principal part from image. It is continued till better quality of Principal component is obtained. Here good quality is obtained by extracting PC. It is explained as continuous signal just for understanding purpose. [14] 2.2 BTC-SPIHT Firstly hybrid algorithm is implemented using BTC & SPIHT. BTC is lossy compression technique and SPIHT is lossless. This hybrid algorithm is used after enhancement.

Block Truncation Coding (BTC):
This technique is used for digitized gray scale images which is very fast and simple lossy Image Compression technique. For compressing digital gray level images BTC uses moment preserving quantization method. Even though this method retains the visual quality of the reconstructed image with high data CR and PSNR

Set Partitioning in Hierarchical Trees (Spiht):
This is powerful wavelet-based image compression method called SPIHT. The original image is passed through DWT block which outputs DWT coefficients of the original image.
Then DWT coefficients are passed through SPIHT encoder which encodes the output and gives data bit stream manner. This bit stream send through SPIHT decoder and passed through IDWT block, which gives original (reconstructed) image back.

Methodology
Key factors of innovative hybrid method are morphological filter & adaptive threshold. This methodology is totally based on multilevel operation. By using this methodology we can achieve required ROI. For Lossy compression oscillation concept and for lossless image compression Quasi fractal technique is used. This technique is developed for achieving better results. Hybrid techniques for compression of an image of brain use following steps as shown in figure 1. [5][6][7][8][9][10][11]

Adaptive Threshold:
In image processing thresholding a greyscale image with a fixed value to get a binary image is mostly used operation. Neighbouring pixel intensities are important for deciding the threshold value at each pixel location. Adaptive thresholding is used for partitioning the original Vol. image into certain sub images and utilize global thresholding techniques for each sub image [12,13].

Algorithm for Quasi Fractal & Oscillation Concept Method
Step 1. Read Input Image Step 2. Convert RGB to Gray image Step 3. Denoise an image Step 4. Resize image into 256 * 256 Step 5. Filtration by using Morphological Filters Step 6. Apply Adaptive thresholding Step 7. Find current ROI Step 8. Repeat procedure by tuning morphological Filter for Refinement.
Step 9. Find Final ROI Step 10. Find ROI Step 11. Apply Lossless Compression Technique over final ROI.
Step 13. Combine O/P images of Step 11& Step 12.

Conclusion
On comparing the results of both hybrid methods, it is observed that Innovative hybrid coding method using oscillation concept & Quasi fractal gives better results than BTC-SPIHT hybrid method. CR is improved from 5.65 to 24.61 which is better. Also PSNR is retained & little bit improved.