Open AccessComputed Tomography Medical Image Compression using Conjugate Gradient
Author(s) -
S.Saradha Rani*,
Gottapu Sasibhushana Rao,
B.Prabhakara Rao
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.l3692.1081219
Subject(s) - conjugate gradient method , gradient descent , artificial intelligence , computer science , nonlinear conjugate gradient method , artificial neural network , image (mathematics) , computer vision , image compression , image quality , medical imaging , compression (physics) , field (mathematics) , pattern recognition (psychology) , image processing , algorithm , mathematics , materials science , composite material , pure mathematics
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.