Hybrid Multimodal Medical Image Fusion Algorithms for Astrocytoma Disease Analysis

Abstract

Astrocytoma is a type of cancer that can form in the brain or spinal cord. It is begins in cells called astrocytes that support nerve cells. Astrocytoma signs and symptoms depend on the location of the tumor. In the analysis of such indicative patients, these tumors of brain can be visualized using a feature based fusion of input images. Multimodality image fusion has played an important role to diagnose the diseases for clinical treatment analysis and enhancing the performance and precision of the computer assisted system. In a recent development of medical field single multimodal medical image cannot provide all the details of human body. For example, the soft tissue information can be represented by magnetic resonance imaging, computed tomography imaging represent the bones dense structure with less distortion. In this paper, proposed method to merge the discrete fractional wavelet transform (DFRWT) with dual tree complex wavelet transform (DTCWT) based hybrid fusion technique for multimodality medical images. The developed fusion algorithm is experienced on the pilot study datasets of patients affected with astrocytoma disease. The fused image conveys the superior description of the information than the source images. Experimental results are evaluated by the number of well-known performance evaluation metrics.