Computed Tomography Medical Image Compression using Conjugate Gradient
Saradha Rani S1, Sasibhushana Rao G2, Prabhakara Rao B3
1Saradha Rani, Dept. of ECE ,GITAM(deemed to be university),Visakhapatnam, Andhra Pradesh, India.
2G.Sasibhushana Rao, Dept. of ECE, AU College of Engineering(A), AU, Visakhapatnam, Andhra Pradesh, India.
3B.Prabhakara Rao, Dept. of ECE, JNT University, Kakinada, Andhra Pradesh,India.
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 5291-5296 | Volume-8 Issue-12, October 2019. | Retrieval Number: L36921081219/2019©BEIESP | DOI: 10.35940/ijitee.L3692.1081219
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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: Image compression which is a subset of data compression plays a crucial task in medical field. The medical images like CT, MRI, PET scan and X-Ray imagery which is a huge data, should be compressed to facilitate storage capacity without losing its details to diagnose the patient correctly. Now a days artificial neural network is being widely researched in the field of image processing. This paper examines the performance of a feed forward artificial neural network with learning algorithm as conjugate gradient. Various update parameters are considered in conjugate gradient methodology. This work performs a comparison between Conjugate gradient technique and Gradient Descent algorithm. MSE and PSNR are used as quality metrics. The investigation is carried on CT scan of lower abdomen medical image.
Keywords: Neural Network, Compression, Gradient Descent, Conjugate Gradient, Performance Metrics
Scope of the Article: Image analysis and Processing