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An Efficient ROI Encoding Based on LSK and Fractal Image Compression

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An Efficient ROI Encoding Based on LSK and Fractal Image Compression 220 The International Arab Journal of Information Technology, Vol. 12, No. 3, May 2015 An Efficient ROI Encoding Based on LSK and Fractal Image Compression TMP Rajkumar 1 and Mrityunjaya Latte 2 1Research Scholar, Anjuman Engineering College, India 2Principal, JSS Academy of Technical Education, India Abstract: Telemedicine is one of the emerging fields in medicine which is characterized by transmitting medical data and images between different users. The medical images which are transmitted over the internet require huge bandwidth. Even images of single patient are found to be very huge in size due to resolution factor and number of images per diagnosis. So, there is an immense need for efficient compression techniques that can be used to compress these medical images. In medical images, only some of the regions are considered to be more important than the others (e.g., tumor in brain Magnetic Resonance Imaging (MRI)). This paper reviews the application of ROI coding in the field of telemedicine. The image coding is done using Wavelet Transform (WT) based on Listless Speck (LSK). The Region of Interest (ROI) is obtained from user interaction and coded with the user given resolution to get high Compression Ratio (CR). In our proposed method, instead of decompressing all the blocks, we decompress only the similar blocks based on the index valued stored on the stack. Thus, our proposed method efficiently compresses the medical image. The performance measure can be analyzed by using Peak Signal to Noise Ratio (PSNR). The execution time of the proposed method will be reduced when compare to the other existing methods. The experimental result shows that the application of ROI coding using LSK brings about high compression rate and quality ROI. Keywords: Image compression, ROI, LSK, fractal image compression, MRI images, iterated functions systems . Received January 28, 2012; accepted August 26, 2012; published online June 26, 2014 1. Introduction with a error tolerance and in such system lossy compression where more suitable. System where Visual communication is becoming increasingly accuracy is prime factor lossy compression schemes important with applications in several areas such as cannot be used. To achieve higher compression with multimedia, communication, transmission and storage lower error modifier version of compression such as of remote sensing images, education and business shape based compression is developed [28]. documents and medical images etc., [18]. In many The compression of medical images has a great situations multiple, large images require processing in demand. The medical community has been very a short period of time [36]. Compression is useful reluctant to adopt lossy algorithms in clinical practice. because it helps reduce the consumption of expensive However, the diagnostic data produced by hospitals resources, such as hard disk space or transmission has geometrically increased and a compression bandwidth [38]. Image compression is achieved by technique is needed that results with greater data reducing redundancy between neighboring pixels but, reductions and hence transmission speed. In these preserving features such as edges and contours of the original image [29]. Increase in the use of color images cases, a lossy compression method that preserves the in the continuous expansion of multimedia applications diagnostic information is needed [35]. Medical images has increased the demand for efficient techniques that used at medical facilities are now commonly can store and transmit visual information. This demand digitalized due to corresponding advances in has made image compression a vital factor and has information technology. Computed Tomography (CT) increased the need for efficient algorithms that can or Magnetic Resonance Image (MRI) generates result in high Compression Ratio (CR) with minimum digitalized signals by its own and diagnostic images loss [1]. There are various methods of compressing from legacy devices can be digitalized by film scanner still images and every method has three basic steps: and such [20]. Lossless compression with progressive Transformation, quantization and encoding [19, 27]. transmission is playing a key role in telemedicine Image compression may be lossy or lossless. applications [24, 25]. Lossless compression is preferred for archival purposes The lossy image compression techniques, which and often for medical imaging, technical drawings, clip reproduce completely acceptable decoded images on art, or comics. This is because lossy compression scenes such as houses or landscapes, do not fulfill the methods, especially when used at low bit rates, strictest quality requirements needed in medical introduce compression artifacts. Various practical applications. There is always the possibility that a applications demands for high date rate compatibility An Efficient ROI Encoding Based on LSK and Fractal Image Compression 221 vague detail might give a reason to suspect some image blocks can be calculated using Euclidean critical changes in a patient’s condition. For this distance. The rest of the paper is organized as follows: reason, the lossy techniques, which tend to give high Section 2 describes some of the recent related works. CRs, such as 1:10-1:30, are not acceptable in medical Section 3 briefs the fractal image compression process. image compression [21]. Image compression is The proposed part is detailed in section 4. required to minimize the storage space and reduction Experimental results and analysis of the proposed of transmission cost. Medical images like MRI and CT methodology are discussed in section 5. Finally, are special images require lossless compression as a concluding remarks are provided in section 6. minor loss can cause adverse effects. Prediction is one of the techniques to achieve high compression. It 2. Related Works means to estimate current data from already known Numerous researches have been proposed by data [26]. Image communication systems for medical researchers for the medical image compression images have bandwidth and image size constraints that process. In this section, a brief review of some result in time-consuming transmission of important contributions from the existing literature is uncompressed raw image data. Thus, image presented. compression is a key factor to improve transmission Ganguly et al . [13] deals with the various aspects speed and storage, but it risks losing relevant medical and types of medical imaging. With the growth of information. It exploits common characteristics of computers and image technology, medical imaging has most images that are the neighboring picture elements greatly influenced the medical field. The diagnosis of a or pixels are highly correlated [1]. It means a typical health problem was highly dependent on the quality still image contains a large amount of spatial and the credibility of the image analysis. redundancy in plain areas where adjacent pixels have El-Rube et al . [11] proposed a contour let-based almost the same values [15]. Medical imaging compression scheme for medical endoscope images. modalities include: The proposed algorithm was compared with two well 1. CT. known transform coding algorithms; the Discrete 2. MRI. Cosine Transforms (DCT) and the Wavelet Transform 3. Ultrasonography (US). (WT). 4. X Radiographs, etc. Tamilarasi and Palanisamy [32] proposed a wavelet based contour let image compression algorithm. These modalities provide flexible means for viewing Recent reports on natural image compression have anatomical cross sections and physiological states. shown superior performance of contour let transform, a Medical images are mostly gray scale images, with new extension to the WT in two dimensions using diagnostically important region in the middle of the Laplacian Pyramid (LP) and directional filter banks. In image and background of the image is usually uniform the diagnosis of medical images, the significant part dark gray [30]. The improved compression Region Of Interest (ROI) was separated out from the performance will be accomplished by making use of rest of the image using fuzzy C-means algorithm and clinically relevant regions as defined by physicians. then to the resultant image optimized contour let Images taken of patients will be aligned to pre-stored transform was applied to enhance the visual quality. image models stored in an atlas. The atlas will contain Bhat et al. [7] proposed a scheme allows achieving models of typical classes of images. If we are trying to a CR up to approximately 40:1 with reasonable image compress a chest x-ray image, then it will be matched quality. with a pre-stored chest x-ray model that is stored in the Yang et al. [39] developed an information hiding atlas [8, 41]. methodology that includes the RSA encryption High quality compression on cardiac MR images algorithm and a DCT based hiding technique. With using wavelet-based methods has been compared with that system, any medical image that would be standard JPEG. Since, the last one is an unrestricted electronically transferred (i.e., emailed, faxed, etc.,) algorithms designed for true-color realistic images, its would have the patient’s information hidden and performance compressing gray level images can be embedded in the image outside of the ROI. improved with alternate algorithms specifically Sumalatha et al . [31] proposed a lossless designed for this purpose [34]. Using image compression approach based on 3D integer wavelet compression techniques, such as JPEG, can no
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