Improved compression techniques for medical images with optimization algorithms

Abstract

In the present era of medical development use of medical images has turned out to be essential for the diagnosis of patients and kept for future reference The main purpose of Digital Imaging and Communications in Medicine DICOM standard is to allow cross vendor interoperability among devices and information systems dealing with digital medical images The modern medical imaging systems and equipments like X Rays Ultrasounds Computed Tomography CT and Magnetic Resonance Imaging MRI support DICOM and use it extensively Medical images are stored in DICOM format Medical imaging equipment creates the DICOM files Each DICOM file not only holds the images but also holds patient information name ID sex and birth date and important acquisition data e g type of equipment used and its settings The reality in today s medical field is that each DICOM file holds a huge amount of data that is difficult to be stored With the intention of resolving this issue medical image compression has become very important for efficient archiving and transmission of images Compression is the process of coding that will effectively reduce the total number of bits needed to represent certain information Currently research in medical image compression concentrates on the implementation of methods such as Run length coding Lempel Ziv Welch LZW Huffman coding Vector Quantization VQ and so on for improved quality of image In case of LZW organization of string table is problematic The conventional Huffman coding needs for initial statistical analysis of the file and it requires transmitting a large decoding table along with the file Because of various code lengths decoding is hard in Huffman coding and in case of Run length coding compression ratio is slow newline