Computed tomography medical image compression using conjugate gradient

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.