Please use this identifier to cite or link to this item:
Title: Enhanced approaches using region of interest based medical image compression
Researcher: Janaki R
Guide(s): Tamilarasi D
Keywords: Computer Science
Medical image compression
Upload Date: 4-Mar-2014
University: Mother Teresa Womens University
Completed Date: 09/12/2013
Abstract: Medical image compression is an area of research which focuses on reducing the amount of space required to store images obtained from advanced medical devices like X-Ray devices, CT / MRI scanners and electron microscope. The compression task in medical imaging plays a vital role in reducing the cost of hardware and software required during storage and transmission. The medical image compression techniques work with two primary objectives, namely, reduced file size and high quality of decompressed image. Reduced file size makes it more suitable for telemedicine and video conferencing applications, while high quality of decompressed images ensures maintenance of relevant information important for diagnosis. Doctors place a high demand of requiring an exact replica of the original image after decompression in a time efficient manner that would help them to provide better healthcare to patients. This demand for fast and efficient coding algorithms in the medical field has led to the development of several techniques which have revolutionized the area of image compression but still the field has a long way to reach maturity. Out of the several techniques proposed, Region of Interest (ROI) based techniques are more frequently used in recent years. ROI-based compression techniques take advantage of both lossy and lossless techniques to compress images. These techniques use lossless techniques on abnormal regions that are important for diagnosis and therefore, require high quality, while using lossy techniques on all the other regions. The method used for determining the ROI in medical images is still an active research area. Development of such ROI-based compression techniques involves three steps. The first step performs ROI and separates the input image into foreground and background. The second step performs a lossless compression on foreground region and the third step performs a lossy compression on background region. The final step combines the results to achieve compression.
Pagination: 236p.
Appears in Departments:Department of Computer Science

Files in This Item:
File Description SizeFormat 
01_title.pdfAttached File91.72 kBAdobe PDFView/Open
02_certificates.pdf138.97 kBAdobe PDFView/Open
03_abstract.pdf11.89 kBAdobe PDFView/Open
04_declaration.pdf121.95 kBAdobe PDFView/Open
05_acknowledgement.pdf128.84 kBAdobe PDFView/Open
06_contents.pdf48.36 kBAdobe PDFView/Open
07_list_of_tables.pdf31.85 kBAdobe PDFView/Open
08_list_of _figures.pdf86.31 kBAdobe PDFView/Open
09_abbreviations.pdf104.59 kBAdobe PDFView/Open
10_chapter 1.pdf433.63 kBAdobe PDFView/Open
11_chapter 2.pdf195.73 kBAdobe PDFView/Open
12_chapter 3.pdf428.82 kBAdobe PDFView/Open
13_chapter 4.pdf775.17 kBAdobe PDFView/Open
14_chapter 5.pdf448.83 kBAdobe PDFView/Open
15_chapter 6.pdf1.69 MBAdobe PDFView/Open
16_chapter 7.pdf2.15 MBAdobe PDFView/Open
17_summary.pdf31.41 kBAdobe PDFView/Open
19_bibliography.pdf197.75 kBAdobe PDFView/Open

Items in Shodhganga are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetric Badge: