Fast Compression For Brain Mr Images With Proposed Algorithms
M.Ramanjaneyulu1, A.V.Narasimha Rao2, M.Balraju3
2Dr. M.Ramanjaneyulu, Research scholar, Department of ECE, JNTUH.
2Dr, A.V.Narasimha Rao, Professor, Department of ECE, CBIT.
3Dr. M. Balraju, Professor, Department of CSE, SVIT.
Manuscript received on 30 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 2656-2661 | Volume-8 Issue-9, July 2019 | Retrieval Number: I8987078919/19©BEIESP | DOI: 10.35940/ijitee.I8987.078919
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Growth in information storage and retrieval significantly depend on images in various domains as the information representation and understand ability is significantly higher. The challenges in processing the complete information in image formats are obtained during storage and transmission. Also, the information extractions from images are significantly difficult compared to information extractions from text. Nonetheless, the incorporation of image analysis for disease detection involves gigantic amount of image data storage, which is a concern of financial drawbacks. Hence, the images used for the analysis must be compressed for storage. However, the complexity of image compression is critical as the information loss can cause significant difference in disease detections. Thus the traditional lossy image compression methods cannot be applied to this problem. Hence, this work addresses the optimal compression of the medical images without vital information loss and with ominously high compression ratio as the second objective of this work.
Keywords: Medical Image Compression, Lossless, Lossy, Segmentation, Fast Compression
Scope of the Article: Parallel and Distributed Algorithms