A SURVEY ON DICOM IMAGE COMPRESSION AND DECOMPRESSION TECHNIQUES

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1 A SURVEY ON DICOM IMAGE COMPRESSION AND DECOMPRESSION TECHNIQUES Shreya A 1, Ajay B.N 2 M.Tech Scholar Department of Computer Science and Engineering 2 Assitant Professor, Department of Computer Science and Engineering, SJB Institute of Technology, Bengaluru, India ABSTRACT: Image compression is an application of data compression that encodes the original image with few bits. The objective of image compression is to reduce the redundancy of the image and to store or transmit data in an efficient form. In this paper we briefly introduce DICOM standard overview. This paper addresses the area of image compression as it is applicable to various fields of image processing. We also briefly describe some compression methods and techniques. Keywords: DICOM, compression, redundancy. [1] INTRODUCTION DICOM stands for Digital Imaging and COmmunications in Medicine. It is an international standard related to the exchange, storage and communication of digital medical images and other related digital data [1]. The DICOM standard covers both the formats to be used for storage of digital medical images and related digital data, and the protocols to be adopted to implement several communication services which are useful in the medical imaging workflow. DICOM was born back in year 1993 by initiative of the American College of Radiology (ACR) and of the National Electrical Manufacturers Association (NEMA). It is often referred to as DICOM 3.0, as it is an evolution of the previous ACR- NEMA 2.0 standard. The main purpose of the DICOM standard is to allow cross-vendor interoperability among devices and information systems dealing with digital medical images, as long as all the involved actors comply with the DICOM standard [4]. The modern medical imaging systems and Equipment s like X-Rays, Ultrasounds, CT (Computed Tomography), and MRI (Magnetic Resonance Imaging) support DICOM and use it extensively. All medical images are stored in DICOM format. The medical imaging equipment s creates the DICOM files. Each DICOM file not only holds the images but also holds patient information (name, ID, sex and birth date), important acquisition data (e.g., type of equipment used and its settings). DICOM has become the de-facto standard in medical imaging: today, the vast majority of digital medical imaging systems of all major vendors (including acquisition devices 186

2 A SURVEY ON DICOM IMAGE COMPRESSION AND DECOMPRESSION TECHNIQUES, diagnostic workstations, archives, servers, medical printers, etc.) support and comply with portions of the DICOM standard, depending on the services they implement [1]. Also, DICOM has been widely accepted and adopted by medical institutions, including public and private hospitals, diagnostic centers and analysis laboratories of different sizes. The reality in today s medical field is that each DICOM file holds a huge amount of data that is difficult to be stored. Hence, we can use a DICOM compression and decompression tool that helps in compressing the medical data and hence reduces time in accessing from central databases or cloud. Here, compression includes both reversible and irreversible (lossless and lossy) techniques depending on users region of Interest. [2] COMPRESSION METHODS [2.1] REVERSIBLE COMPRESSION (LOSSLESS) Upon decompression, the image is perfectly reconstructed and numerically identical to the original (i.e. the original and decompressed are perfectly correlated) [2]. Figure 1: Reversible compression Lossy image compression is a three step algorithm (Fig 1): In the first step the original image is transformed in order to eliminate the inter-pixel redundancy. Then, quantization is done to remove psycho-visual redundancy. The bits are then encoded to get more compression from the coding redundancy. [2.2] IRREVERSIBLE COMPRESSION (LOSSY) With irreversible compression (Fig 2), data are discarded during compression and cannot be recovered. Upon compression frequency content to which the human eye is insensitive is removed [2]. Upon decompression, the discarded information cannot be recovered, resulting in some reconstruction interpretation. 187

3 Figure 2: Irreversible compression Lossless image compression is a two-step algorithm: In the first step the original image is transformed in order to eliminate the inter-pixel redundancy. In the second step, an entropy coder is used to remove coding redundancy. [3] LOSSLESS COMPRESSION TECHNIQUES [3.1] RUN LENGTH ENCODING This is a very simple compression method used for sequential data. It is very useful in case of repetitive data. This technique replaces sequences of identical symbols (pixels), called runs by shorter symbols [3]. The run length code for a gray scale image is represented by a sequence {Vi, Ri} where Vi is the intensity of pixel and Ri refers to the number of consecutive pixels with the intensity Vi as shown in the figure. If both Vi and Ri are represented by one byte, this span of 12 pixels is coded using eight bytes yielding a compression ratio n of 1: 5. Figure 3: Run Length Encoding [3.2] HUFFMAN ENCODING GIFF This is a general technique for coding symbols based on their statistical occurrence frequencies (probabilities). The pixels in the image are treated as symbols. The symbols that occur more frequently are assigned a smaller number of bits, while the symbols that occur less frequently are assigned a relatively larger number of bits. Huffman code is a prefix code [3]. This means that the (binary) code of any symbol is not the prefix of the code of any other symbol. Most image coding standards use lossy techniques in the earlier stages of compression and use Huffman coding as the final step. [3.3] LZW CODING LZW (Lempel- Ziv Welch) is a dictionary based coding. Dictionary based coding can be static or dynamic. In static dictionary coding, dictionary is fixed during the encoding and 188

4 A SURVEY ON DICOM IMAGE COMPRESSION AND DECOMPRESSION TECHNIQUES decoding processes. In dynamic dictionary coding, the dictionary is updated on fly [3]. LZW is widely used in computer industry and is implemented as compress command on UNIX. [3.4] AREA CODING Area coding is an enhanced form of run length coding, reflecting the two dimensional character of images. This is a significant advance over the other lossless methods. For coding an image it does not make too much sense to interpret it as a sequential stream, as it is in fact an array of sequences, building up a two dimensional object. The algorithms for area coding try to find rectangular regions with the same characteristics. These regions are coded in a descriptive form as an element with two points and a certain structure. This type of coding can be highly effective but it bears the problem of a nonlinear method, which cannot be implemented in hardware. Therefore, the performance in terms of compression time is not competitive, although the compression ratio is. [4] LOSSY COMPRESSION TECHNIQUES [4.1] TRANSFORMATION CODING In this coding scheme, transforms such as DFT (Discrete Fourier Transform) and DCT (Discrete Cosine Transform) are used to change the pixels in the original image into frequency domain coefficients (called transform coefficients).these coefficients have several desirable properties [3]. One is the energy compaction property that results in most of the energy of the original data being concentrated in only a few of the significant transform coefficients. This is the basis of achieving the compression. Only those few significant coefficients are selected and the remaining are discarded. The selected coefficients are considered for further quantization and entropy encoding. DCT coding has been the most common approach to transform coding. It is also adopted in the JPEG image compression standard. [4.2] VECTOR QUANTIZATION The basic idea in this technique is to develop a dictionary of fixed-size vectors, called code vectors. A vector is usually a block of pixel values. A given image is then partitioned into non-overlapping blocks (vectors) called image vectors. Then for each in the dictionary is determined and its index in the dictionary is used as the encoding of the original image vector. Thus, each image is represented by a sequence of indices that can be further entropy coded. [4.3] FRACTAL CODING The essential idea here is to decompose the image into segments by using standard image processing techniques such as color separation, edge detection, and spectrum and texture analysis. Then each segment is looked up in a library of fractals. The library actually contains codes called iterated function system (IFS) codes, which are compact sets of numbers. Using a systematic procedure, a set of codes for a given image are determined, such that when the IFS codes are applied to a suitable set of image blocks yield an image that is a very close approximation of the original [3]. This scheme is highly effective for compressing images that have good regularity and self-similarity. [4.4] BLOCK TRUNCATION CODING In this scheme, the image is divided into non overlapping blocks of pixels. For each block, threshold and reconstruction values are determined. The threshold is usually the mean of the pixel values in the block. Then a bitmap of the block is derived by replacing all pixels whose values are greater than or equal (less than) to the threshold by a 1 (0). Then for each segment (group of 1s and 0s) in the bitmap, the reconstruction value is determined. This is the average of the values of the corresponding pixels in the original block. 189

5 [4.5] SUB BAND CODING In this scheme, the image is analyzed to produce the components containing frequencies in well-defined bands, the sub bands. Subsequently, quantization and coding is applied to each of the bands. The advantage of this scheme is that the quantization and coding well suited for each of the sub bands can be designed separately. [5] BENEFITS OF COMPRESSION It provides a potential cost savings associated with sending less data over switched telephone network where cost of call is really usually based upon its duration. It not only reduces storage requirements but also overall execution time. It also reduces the probability of transmission errors since fewer bits are transferred. It also provides a level of security against illicit monitoring. [6] CONCLUSION This paper presents the overview of DICOM standard and various types of image compression techniques. Compression provides a potential cost savings associated with sending less data over switched telephone network where cost of call is really usually based upon its duration. It also reduces the probability of transmission errors since fewer bits are transferred. There are basically two types of compression techniques. One is Lossless Compression and other is Lossy Compression Technique. This paper also presents the various lossy compression methods and lossless compression methods. REFERENCES [1] Mario Mustra, Kresimir Delac, Mislav Grgic, Overview of the DICOM Standard Techniques, University of Zagreb, Faculty of Electrical Engineering and Computing Department of Wireless Communication Unska 3/XII, HR Zagreb, Croatia. [2] Sonal, Dinesh Kumar, A Study of Various Image Compression Techniques, Department of Computer Science & Engineering Guru Jhambheswar University of Science and Technology, Hisar [3] Mrs.Bhumika Gupta Study Of Various Lossless Image Compression Technique, Computer Science Engg. Dept. G.B.Pant Engg. College Pauri Garhwal,Uttrakhand. [4] Digital Imaging and Communications in Medicine (DICOM), NEMA Publications, "DICOM strategic document", Ver. 8.0, April 2008, available at: 190