The background pixels of all the medical image are low values, and they differ by +3 or −3. The RLE is used for medical image compression in hybrid approach, where gray scale value gives certain interesting fact about the distribution in image. DICOM support lossless compression schemes like run-length encoding, Huffman coding, LZW coding, area coding, and arithmetic coding. There are two main categories of compression lossless (reversible) and lossy (irreversible). An increasing volume of data generated by new imaging modality, CT scan and MRI lossy compression technique are used to decrease the cost of storage and increase the efficiency of transmission over networks for teleradiology application. The survey paper conveys that compression ratio 4:1 is possible using lossless compression. Thus, medical image compression application development is a challenging problem. The medical imaging application required lossless image compression. The lossless image has huge application in archival of medical and digital radiography document, where loss of information in original image could consider improper diagnosis. Two types of compression methods are classified. The DICOM file format is created by addition of header size and pixel data. DICOM file format design consideration is based on the following concept such as pixel depth, photometric interpretation, metadata, and pixel data. The DICOM format includes some information that can be useful for image registration, such as position and orientation of the image with respect to the data acquisition device and patient information with respect to voxel size. The major file format currently useful in medical imaging is DICOM format. The task of the image file format is to provide a standardized way to store the unique data in a much organized and systematic manner and showcase how the pixel data understood the correct loading, visualization, and analysis was derived by the software. There are four major file formats in medical imaging, and they are Neuroimaging Informatics Technology Initiative (NIfTI), Analyze, DICOM, and MINC. The third is image pixel intensity data it contains necessary medical image data display like number of frames, lines, columns, etc.
Each data element has four fields these are tag, value representation, value length, and value field. The second is data set it consists of multiple set of data elements. The first is header it consists of 128 bytes of file preamble which is followed by string by 4-byte prefix, and it contains four-character string. Hence DICOM standards are widely used in the integration of digital imaging systems in medicine. DICOM file contains both a header, which include text information such as patient’s name, modality, image size, etc., and image data in the same file. The DICOM technology is suitable when sending images between different departments within hospitals and/or other hospitals and the consultant. The DICOM standard has been developed by ACR-NEMA to meet the needs of manufacturers and users of medical imaging equipment for interconnection of devices on standard networks. DICOM (Digital Imaging and Communications in Medicine) makes medical image exchange easier and independent of the imaging equipment manufacturer. Since there are multiple medical equipment manufacturers, there is a strong need to develop a standard for storage and exchange of medical images. There has been a huge development in noninvasive medical imaging equipment. The objective of this research work is to improve compression ratio and compression gain.ĭigital technology has, in the last few decades, entered in almost every aspect of medicine. This work is proposed to examine the efficiency of different wavelet types and to determine the best. The lossless hybrid encoding algorithm, which combines run-length encoder and Huffman encoder, has been used for compression and decompression purpose. Using this approach by applying N-level decomposition on 2D wavelet types like Biorthogonal, Haar, Daubechies, Coiflets, Symlets, Reverse Biorthogonal, and Discrete Meyer, various levels of wavelet coefficients are obtained. This implemented work represents discrete wavelet-based threshold approach.
There is an unrelenting need in our medical community to develop applications that are low on cost, with high compression, as huge number of patient’s data and images need to be transmitted over the network to be reviewed by the physicians for diagnostic purpose. Maintaining human healthcare is one of the biggest challenges that most of the increasing population in Asian countries are facing today.
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