Modified dual channel PCNN algorithm with hybrid edge enhancement approach for multimodality brain image fusion. (2018)

• Record Type: Journal Article
• Authors: Kavitha, S.; Bharathi, B.; Sasikala, P.; Chandraleka, D.; Ashwini, V.
• Is Part Of: International journal of biomedical engineering and technology. Volume 28:Number 2(2018)
• Page Start: 120
• Page End: 146
• Publication Date: 2018
• Subjects: image fusion -- dual channel pulse coupled neural network -- multimodality brain images -- Canny algorithm -- ant colony optimisation; Biomedical engineering -- Periodicals; 610.28
• Languages: English
• ISSNs: 1752-6418

Abstract

Image fusion plays a vital role for many applications in the field of computer vision, remote sensing, image robotics and medical imaging. This paper is focused on the fusion of multimodality brain images, using a Modified Dual Channel Pulse Coupled Neural Network (MDCPCNN) algorithm along with hybrid edge enhancement approach namely Canny and Ant Colony Optimisation (ACO). In general, the fused image derived from PCNN algorithm has better tissue information and contrast even though a loss occurs in edge information. To overcome this drawback, a hybrid edge enhancement approach is proposed and applied along with MDCPCNN fusion algorithm. The proposed model is validated using four datasets of brain images from different modality, with the subjective and objective measures. The fused image constructed from the proposed model consistently retains the edge information, contrast and texture than the existing PCNN's without false information or information loss.