Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/299487
Title: Improved compression techniques for medical images with optimization algorithms
Researcher: Manimekalai M A P
Guide(s): Vasanthi N A
Keywords: Engineering and Technology
Computer Science
Imaging Science and Photographic Technology
Compression techniques,
Optimization algorithms
Medical images
University: Anna University
Completed Date: 2019
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
Pagination: xx,124p.
URI: http://hdl.handle.net/10603/299487
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File24.98 kBAdobe PDFView/Open
02_cerrtificates.pdf531.81 kBAdobe PDFView/Open
03_abstracts.pdf109.92 kBAdobe PDFView/Open
04_acknowledgements.pdf678.86 kBAdobe PDFView/Open
05_contents.pdf101.38 kBAdobe PDFView/Open
06_listoftables.pdf5.91 kBAdobe PDFView/Open
07_listoffigures.pdf57.49 kBAdobe PDFView/Open
08_listofabbreviations.pdf165.73 kBAdobe PDFView/Open
09_chapter1.pdf169.94 kBAdobe PDFView/Open
10_chapter2.pdf238.41 kBAdobe PDFView/Open
11_chapter3.pdf480.41 kBAdobe PDFView/Open
12_chapter4.pdf686.56 kBAdobe PDFView/Open
13_chapter5.pdf432.23 kBAdobe PDFView/Open
14_conclusion.pdf18.11 kBAdobe PDFView/Open
15_references.pdf100.48 kBAdobe PDFView/Open
16_listofpublications.pdf65.21 kBAdobe PDFView/Open
80_recommendation.pdf145.9 kBAdobe PDFView/Open
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