TY  - JOUR
T1  - Fusion of CT and MRI Images Using OpenCV with FL2440 Hardware
AU - Sivakumar, N. AU - Helenprabha, K. 
JO  - Asian Journal of Information Technology
VL  - 15
IS  - 12
SP  - 1936
EP  - 1944
PY  - 2016
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2016.1936.1944
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2016.1936.1944
KW  - Image segmentation
KW  -image fusion
KW  -OpenCV
KW  -kernel optimization
KW  -PCA
AB  - Multiple acquisition schemes are utilized to acquire complementary informative description associated with an object. Image fusion improve the features of an image (depicting comprehensive description of the object) acquired from different acquisition schemes. The existing homogeneous schemes fail to assign membership to voxels thereby yielding noisy boundaries in the core. In this work, a novel scheme to optimize the kernel is done through class separability criterion using OpenCV tools on hardware (s3c2440a processor) and the algorithms are scalable and generic. PCA based fusion intrinsic dimensionality reduction that uses optimal set of parameters required to account the experiential properties of the data is used. The Bigram Proximity Matrix captures the number of times a feature pair occurs with its columns and rows representing the features of CT and MRI scanned images. The metrics used to evaluate the performance includes MSE, PSNR and Cross entropy. Improved results are achieved with minimum MSE, minimum cross entropy and maximum PSNR.
ER  -