# Compression of Medical Images Using Deflate Algorithm

Topics: Data compression, JPEG, Huffman coding Pages: 21 (7853 words) Published: June 16, 2013
CHAPTER TWO
2.0 LITERATURE REVIEW
3.1 IMAGES
An image may be defined as a rectangular array of pixels. The pixel of a grayscale image is a nonnegative integer interpreted as the intensity (brightness, luminosity) of the image (Deever and Hemami, 2003). It is a picture that has been created or copied and stored in electronic form. An image can be described in terms of vector graphics or raster graphics . An image stored in raster form is sometimes called a bitmap (Margaret, What is image? - Definition from WhatIs.com:, 2005) According to Magaret Rouse, Raster graphics are digital images created or captured (for example, by scanning in a photo) as a set of samples of a given space. A raster is a grid of x and y coordinates on a display space (And for three-dimensional images, a z coordinate.) A raster image file identifies which of these coordinates to illuminate in monochrome or color values. The raster file is sometimes referred to as a bitmap because it contains information that is directly mapped to the display grid. Examples of raster image file types are: BMP, TIFF, GIF, and JPEG files (Margaret, What is raster graphics? - Definition from WhatIs.com, 2005) Vector graphics is the creation of digital images through a sequence of commands or mathematical statements that place lines and shapes in a given two-dimensional or three-dimensional space. In physics, a vector is a representation of both a quantity and a direction at the same time. In vector graphics, the file that results from a graphic artist's work is created and saved as a sequence of vector statements. For example, instead of containing a bit in the file for each bit of a line drawing, a vector graphic file describes a series of points to be connected. One result is a much smaller file (Magaret, 2006) Digital images are characterized by multiple parameters. The first feature of a digital image is its color mode. A digital image can have one of three modes: binary, grayscale or color. A binary (bilevel) image is an image in which only two possible values for each pixel. A grayscale image means that its each pixel can contain only a tint of gray color. A digital image is a set of pixels. Each pixel has a value that defines color of the pixel. All the pixels are composed into one array (Rapinder and Kaushal, 2010) 3.2.1 Images Formats

There are various formats of images ranging from TIF, JPG, PNG, GIF (which are the most common ones known and used on internet websites), though there are many others, yet these are just few of the most popular images format used today. 3.2.2.1 Joint Photographic Expert Group (JPEG)

JPG (JOINT PHOTOGRAPHIC EXPERT GROUP) FORMAT is mostly used on digital cameras and webpages because of its historical data compression rate which is very high. However JPG uses lossy compression method which allows for compression to a very low size but with reduced image quality. JPG file format makes image more available on portable devices because the processing rate of images in this format is considerably minimal. Also due to its reduced size characteristics, it tends to make image transfer more convenient and time saving (Wayne, 2010). 3.2.2.2 Tagged Image (File) Format (TIFF)

TIF is lossless (including LZW compression option), which is considered the highest quality format for commercial work. The TIF format is not necessarily any "higher quality" per se (the image pixels are what they are), and most formats other than JPG are lossless too. This simply means there are no additional losses or JPG artifacts to degrade and detract from the original. And TIF is the most versatile, except that web pages don't show TIF files. For other purposes however, TIF does most of anything you might want, from 1-bit to 48-bit color, RGB, CMYK, LAB, or Indexed color (Wayne, 2010).

TIFF was created by Aldus for ‘desktop publishing’, and by 2009 it was transferred to the control of Adobe Systems. TIFF is popular...

References: Alagendran, B., and Manimurugan, S. (2012). A Survey on Various Medical Image Compression Techniques. International Journal of Soft Computing and Engineering (IJSCE), 2, 43-43.
Alarabeyyat, A. (2010). Lossless Image COmpression Techniques Using Combination Method.
Al-Hashemi, R., and Kamal, I. (2011). A New Lossless Image Compression Technique Based on Bose. International Journal of Software Engineering and Its Applications, 5(3), 15-22.
Al-Hashemi, R., and Kamal, I. W. (2011, July). A New Lossless Image Compression Technique Based on Bose, Chandhuri and Hocquengham (BCH) Codes. International Journal of Software Engineering and Its Applications, 5 No. 3, 15-22.
Andrew, G. (2002, April 13). An Explanation of the 'Deflate ' Algorithm. Retrieved April 23, 2013, from www.zlib.net: http://www.zlib.net/feldspar.html
Aswin, K
Avramović, A., and Savić, S. (2011, February). Lossless Predictive Compression of Medical Images*. SERBIAN JOURNAL OF ELECTRICAL ENGINEERING, 8, 27-36.
Bahadili, H., and Rababa 'a, A. (2010). A Bit-Level Text Compression Scheme Based on HCDC Algorithm. International Journal of Computer and Applications, 32(3).
Chandra, N. S., Raju, M. B., Arya bahanu, M., and Vikram, B. R. (2009). Binary merge coding for lossless image data compression. Journal of Computer Science, 5(5), 388-391.
Chen, R. -C., Pai, P. -Y., Chan, Y. -K., and Chang, C. -C. (2009). Lossless Image Compression Based on Multiple-Tables Arithmetic Coding. Mathematical Problems in Engineering, 2009.
Chen, Y.-Y. (2007). Medical image compression using DCT-based sub band decomposition and modified SPIHT data organization. International journal of medical informatics, 76, 717-725.
Cho, S., Kim, D., and Pearlman, W. A. (2004, March). Lossless Compression of Volumetric Medical Images with Improved Three-Dimensional SPIHT Algorithm. Journal of Digital Imaging, 17(1), 57-63.
Deever, A., and Hemami, S. (2003). Lossless image compression with projection-based and adaptive reversible integer wavelet transforms. IEEE Transactions on ImageProcessing, 12, 489-499.
Elias, P. (1955). Coding for noisy channels. IRE Convetion Record, 4, 37-46.
Franti, P. (1993). A fast and Efficient Compression Method for Binary Image.
Howard, P. G., and Scott, V. J. (1992). New Method for Lossless Image Compression Using Arithmetic Coding. Information Processing and Management, 286, 749-763.
Jagadish, H. P., and Lohit, M. K. (2010). A NEW LOSSLESS METHOD OF IMAGE COMPRESSION AND DECOMPRESSION USING HUFFMAN CODING TECHNIQUES. Journal of Theoretical and Applied Information Technology, 18.
Jean-loup, G. (1999, September 5). comp.compression Frequently Asked Questions. Retrieved March 3, 2013, from Faqs.org: http://www.faqs.org/faqs/compression-faq/part2/
Jiang, H., ZhiyuanMa, Hu, Y., Yang, B., and Zhang, a
Koff, D. A., MD, and Shulman, H. (2006). An Overview of Digital Compression of Medical Images: Can We Use Lossy Image Compression in Radiology? CARJ, 57, 211-217.
Magaret, R. (2006, February). What is vector graphics? - Definition from WhatIs.com:. Retrieved February 15, 2013, from WhatIs.com: http://searchwindevelopment.techtarget.com/definition/vector-graphics
Margaret, R
Margaret, R. (2005, September). What is raster graphics? - Definition from WhatIs.com. Retrieved February 15, 2013, from WhatIs.com: http://searchcio-midmarket.techtarget.com/definition/raster-graphics
Meyer, B., and Tischer, P
Muthaiah, R., NeelaKantan, K., Sharma, V., and Arora, A. (2008). Image compression and reconstruction using cubic spline interpolation technique. American Journal of Applied Sciences, 5(11).
Nadarajan, K., and Zukarnain, Z. A. (2008). Analysis of string matching compression algorithm. Journal of Computer Science, 4(3), 205-210.
Neil, C. (2012). Huffman compression in the Deflater. Retrieved April 24, 2013, from www.javamex.com: http://www.javamex.com/tutorials/compression/deflater_algorithm2.shtml#.UXfC8rWG2Kg
Neil, C
Perlmutter, e. a. (1998). Medical Image Compression and Vector Quantization. Statistical Science (13), 1, 50-53.
Pujar, J. H., and Kadlaskar, L. M. (2010). A new Lossless Method of Image Compression and Decompression using Huffman Coding Technique. 15(1), 1-*.
Rachel, A. (2011, January). Different Image Formats - And When to Use Them. Retrieved February 15, 2013, from 1stwebdesigner: http://www.1stwebdesigner.com/design/different-image-formats/
Ramakrishnan, B., and Sriraam, N
Rapinder, K., and Kaushal, N. (2010). Comparative Analysis of Various Compression Methods for Medical images. National Institute of Technical Teachers’ Training and Research, 1-5.
S.M.Ramesh, and A.Shanmugam. (2010). Medical Image Compression using Wavelet Decomposition for Prediction Method. (IJCSIS) International Journal of Computer Science and Information Security, 7, 262-265.
Saravanan, C., and Ponalagusamy, R. (2009). Lossless Grey-scale Image Compression using Source Symbols Reduction and Huffman Coding. International Journal of Image Processing, 3(5).
Shannon, C. E. (1948, july, October). A Mathematical Theory of Communication. the Bell System Technical Journal, 27, 379-423, 623-656.
Sharma, D. K., Gaur, loveleen, and Okunbor, D. (2005). Image compression and feature extraction using kohonen 's SOM neural network. Journal of Strategic E-Commerce, 5(1).
Starosolski, R. (2006, December 20). Simple Fast and Adaptive Lossless Image Compression Algorithm. Software Practice and Experience, 1-28.
Talu, M. F., and Turkoglu, I. (2003). Hybrid lossless compression method for binary images. 1-9.
Wayne, F. (2010). Image file formats - TIF, JPG, PNG, GIF. Retrieved February 15, 2013, from SCANTIPS: http://www.scantips.com/basics09.html
Wei, W.-Y
Weinberger, M., and Seroussi, G. (1999). The LOCO-I Lossless Image Compression Algorithm: Principles and Standardization into JPEG-LS. IEEE Int’l Conference on Image Processing, 68-72.
Wheeler, M. B. (1994). A Block-Sorting Loss-less Data Compression Algorithm. Systems Research Center, 22(5).
Zhou, L. (2004). A New Highly Efficient Algorithm for Lossless Binary Image Compression. Master Thesis,.
Zukoski, J., M., Boult, Terrance, and Iyriboz, T. (2006). A novel approach to medical image compression. Int. J. Bioinformatics Research and Applications, 2(1).