Jour of Adv Research in Dynamical & Control Systems, May 2017 Special Issue on Recent Trends in Engineering and Managerial Excellence The Secure Lossless Compression Scheme for Grayscale Medical Images Using PBT and Modified Steganography C. Narmatha, Department of Electronics and Communication Engineering, Karpagam University, Coimbatore, India. E-mail:cmnarmatha@gmail.com P. Manimegalai, Department of Electronics and Communication Engineering, Karpagam University, Coimbatore, India. E-mail:manimegalai.vairavan@gmail.com S. Manimurugan, College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia. E-mail:semanimurugan@gmail.com Abstract--- This paper presents a new and secure compression scheme without the loss of data. The proposed scheme is developed for grayscale medical images. It has been segregated into two divisions/phases. In phase-I, the secret medical image is encoded by the proposed modified steganography. In phase-II, the encoded image is compressed by the proposed pixel block compression technique. This paper is describes the phase-II of encoded image compression. The proposed compression is divided into four types of processes. Those processes are segregation, shuffling, conversion and encode. The significant of this technique is that, the image pixels positions are interchanged as much as possible within the image itself. Due to these processes, the proposed technique is achieved the high complexity. However, it’s not an easy task to retrieve the same image by third parties. In addition, the proposed compression technique is providing lossless compression, 60% of the size can be reduced from the original image size, minimum execution time than the existing methods and the exact replica (100%) of the image can be retrieved from the reconstruction process. Index Terms--- Steganography, Pixel Based Technique, Grayscale Medical Image, Compression, Decompression. I. Introduction This paper is presents an extend version of the pixel based technique (PBT) [11]. S. Manimurugan and etc.al., had proposed a secure medical image Lossless Compression schemes. The Confidentiality, Integrity and Availability (CIA) property of a medical image had also been proven by the experimental results [1, 2, 8, 9 and 10]. The same author had focused on two foremost near to lossless compressions of JPEG-LS and Wavelet with his teammates. After precise experimentations, the chosen algorithms, were producing the magnificent results based on the situations for grayscale images, the proposed methods had been achieved 90% of lossless compression, it means near to lossless compression. The quality of the image, both chosen methods are up to the mark and differences of the quality decibel units (DB) are minimum [6]. Yang-Gi Wu and others had proposed on image compression in different ways. In his work, the image must be compressed before transmission and storage. To achieve better compression the spectral domain is used from the spatial domain transform. In another paper the author said that, the discrete cosine transform was using as a band pass filter to decompose a sub-block into equal sized-bands [12,13]. Christofer Schwartz and Marcelo da Silva Pinho had proposed a practical problem in the optical image compression scope captured by remote-sensing satellites with energy restriction. A large number of embedded CPU architectures have a multicore capability. The results shown several gained in the processing time, especially for the SPIHT algorithm, which used only 5% of the original processing time and 12.8% of energy expenditure (for a compression rate of 1.5 bit per pixel), for the platforms evaluated using this scheme [4]. Andrew Martchenko and Guang Deng had proposed, Adaptive Predictor Combination (APC) was a framework for combining multiple predictors for lossless image compression and was often at the core of state-of-the-art algorithms. In that paper, a Bayesian parameter estimation scheme was proposed for APC. Extensive experiments using natural, medical, and remote sensing images of 8-16 bit per pixel had confirmed that the predictive performance was consistently better than that of APC for any combination of fixed predictors and with only a marginal increase in computational complexity [7]. ISSN 1943-023X 96