Integrated HNAS Network Model Based Lossless Compression with Data Hiding using Parity Check in Medical Images
Lakshmanan S1, Mary Shanthi Rani M2
1S. Lakshmanan, Department of Computer Science and Applications, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Dindigul, Tamil Nadu, India.
2M. Mary Shanthi Rani*, Department of Computer Science and Applications, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Dindigul, Tamil Nadu, India. Email:
Manuscript received on January 12, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 2656-2662 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1756029420/2020©BEIESP | DOI: 10.35940/ijitee.D1756.029420
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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: A massive volume of medical data is generating through advanced medical image modalities. With advancements in telecommunications, Telemedicine, and Teleradiology have become the most common and viable methods for effective health care delivery around the globe. For sufficient storage, medical images should be compressed using lossless compression techniques. In this paper, we aim at developing a lossless compression technique to achieve a better compression ratio with reversible data hiding. The proposed work segments foreground and background area in medical images using semantic segmentation with the Hierarchical Neural Architecture Search (HNAS) Network model. After segmenting the medical image, confidential patient data is hidden in the foreground area using the parity check method. Following data hiding, lossless compression of foreground and background is done using Huffman and Lempel-Ziv-Welch methods. The performance of our proposed method has been compared with those obtained from standard lossless compression algorithms and existing reversible data hiding methods. This proposed method achieves better compression ratio and a hundred percent reversible when data extraction. Keywords : Lossless Compression, Semantic segmentation, HNAS Nets, Reversible Data Hiding.
Keywords: Free Convection, Heat Transfer, Liquid Metal, Mixed Convection, MHD
Scope of the Article: Natural Language Processing