Texture Aware Deep Feature Map Based Linear Weighted Medical Image Fusion

Abstract

Medical image analysis is a critical job for clinicians and radiologists to attain minute insights for proper diagnosis. The presence of complementary details of the region of interest (ROI) from multiple medical imaging modalities instigates the researchers to integrate or combine the pathological details for the ease of clinical diagnosis. In this paper, the objective is to obtain a comprehensive image that presents composite image details from the two multimodal images of the same ROI. The basic idea is to generate robust fusion weights in the form of individually weighted matrices that could potentially superintend the fused outcome from the input image matrices. The extraction of texture features comes into play with the employment of the fast gray level co-occurrence matrix-mean technique. The feature maps of the source images are derived from the convolution layers on which the texture analysis is done to evaluate a weight map. Linear weights-based spatial domain fusion is employed using the weight map. Post auditioning several relevant fusion strategies and baseline hyper-parameter tuning, the obtained sets of outputs are validated via objective analysis in terms of standard metrics and compared with other fusion methods.

Keywords

• Feature map
• GLCM
• medical image fusion
• texture map
• deep learning