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Performance Evaluation of ROI-based Image Compression Techniques
« on: November 23, 2011, 07:46:04 am »
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Author : Gunpreet Kaur, Mandeep Kaur
International Journal of Scientific & Engineering Research Volume 2, Issue 10, October-2011
ISSN 2229-5518
Download Full Paper : PDF

Abstract- Medical diagnostic data produced by hospitals has increased exponentially. The coming era of digitized medical information and film-less imaging, has made it a challenge to deal with the storage and transmission requirement of enormous data. With this, selective medical image compression, a technique where explicitly defined regions of interest are compressed in a lossless way whereas image regions containing unimportant information are compressed in a lossy manner are in demand, day by day. Such techniques are of great interest in telemedicine which is a rapidly developing application of clinical medicine, where medical information is transferred through interactive audiovisual media. Archiving and retaining these data for at least more than two years is expensive, difficult and requires sophisticated data compression techniques. In the current research work, the focus has been solely on the performance evaluation on the ROI-based compression of medical images, but in a different prospective.  The Mammogram images are used for the study. The image is divided into regions; ROI and the background. Then the arbitrary shape ROI breast region is compressed losslessly using losses image compression algorithms like SPIHT, JPEG2000 and Adaptive SPIHT. The background can be discarded or compressed as userís will. The work also introduces an ROI medical image compression technique that is able to assign priorities in case of multiple ROIs. Experimental results show that the proposed method offer potential advantages like extraction and integration of arbitrary shaped ROI, energy efficiency, ROI priority etc. in medical applications of digital mammography applications.

Keywords- Adaptive SPIHT, digital mammography, JPEG2000, SPIHT, Wavelets

I.   INTRODUCTION
The American Cancer Society (ACS) [1] indicates that the probability of developing invasive breast cancer for USAís women younger than 39 is 1 in 210, and aged from 40 to 59 is 1 in 26. Mammography is the most effective way of detecting breast cancer before the onset of clinical symptoms [2]. The latest digital devices used in medical scenarios capture mammograms and angiograms images with a bit-depth resolution of 8-, 12- or 16-bits per pixel. In some cases, this high bit-depth resolution may produce files that grow to as much as 200 MB per mammography. Considering that current ACS guidelines for breast cancer screening recommend one annual mammography for women over 40 years of age, the increment in cost of both the transmission and storage capacity for mammographies is rising every year. A medical center that produces 20 mammograms per day, for instance, requires storage capabilities of more than 4 GB per day [3], and of more than 1.4 TB per year.
 This brings a huge challenge to the current medical system. Compression is one of the indispensible techniques to solve this problem.  For most medical images (digital mammograms), the diagnostically significant information is localized over relatively small regions of interest. In practice, the compression of medical images (digital mammograms) must be reliable because a minor loss may result in a serious consequence.
Due to clinical needs, lossless compression of medical images is often a sensible choice. Basically image compression techniques have been classified into two main categories namely: lossy and lossless methods. Lossy compression methods cannot achieve exact recovery of the original image, but achieves significant compression ratio. Lossless compression techniques, as their name implies, involve no loss of information. The original data can be recovered exactly from the compressed data. The fundamental goal of image compression is to reduce the bit-rate for transmission or storage while maintaining an acceptable fidelity or image quality.
One of the most successful applications of wavelet methods is transform-based image compression. The overlapping nature of the wavelet transform alleviates blocking artifacts, while the multiresolution character of the wavelet decomposition leads to superior energy compaction and perceptual quality of the decompressed image.
Previously, a new, fast and efficient image codec [4] based on set partitioning in hierarchical trees was proposed. This algorithm uses the principles of partial ordering by magnitude, set partitioning by significance of magnitude with respect to a sequence of octavely decreasing thresholds, ordered bit plane transmission, and self-similarity across scale in an image wavelet transmission. In 1996, the JPEG committee began to investigate possibilities for a new still image compression standard to serve current and future applications. This initiative was named JPEG2000 [5]. Selective medical image compression [6] is  achieved by extracting region of diagnostic importance in course of achieving energy efficiency, then coding ROI and the background with a combination of JPEG2000 and SPIH. More recently, JPEG2000 ROI coding through component priority for digital mammography [7], has introduced a ROI coding method that is able to prioritize multiple ROIs at different priorities guaranteeing lossy-to-lossless coding.   

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