Back close

Edge Preserving Image Fusion Using RMS Contrast and Linear Prediction Model

School: School of Engineering

Project Incharge:Mrs.Hema P Menon
Co-Project Incharge:Sreeja S
Edge Preserving Image Fusion Using RMS Contrast and Linear Prediction Model

This work focuses on enhancing the visual quality of medical images obtained using X-Ray, MRI and CT modalities through edge preserving image fusion techniques. This would be helpful for doctors in analysis and treatment planning, as it provides a better visualization of the images under consideration.  The fusion of the images has done using Root Mean Square (RMS) based perceptual contrast measure and a linear prediction (LP) based edge extraction method. To assess the performance of the proposed fusion scheme, the results obtained are analyzed using the following quality metrics say, Universal Image Quality Index, Structural Similarity Index Measure (SSIM), Fusion Mutual Information (FMI), Structural Content and Entropy Measure, which are suitable when the ground truth is not available for comparison.

Related Projects

ACIST : Amrita Content based Indexing and Searching Tool
ACIST : Amrita Content based Indexing and Searching Tool
WINSOC – Wireless Sensor Networks with Self-Organization Capabilities for Critical and Emergency Applications
WINSOC – Wireless Sensor Networks with Self-Organization Capabilities for Critical and Emergency Applications
Search and Rescue Robot
Search and Rescue Robot
Kernel Based Approaches for Context Based Image Annotation
Kernel Based Approaches for Context Based Image Annotation
Hand Orthotic Device
Hand Orthotic Device
Admissions Apply Now