Discrete Wavelet Transformation based Multimodal Medical Image Fusion for Disease Identification

Abstract

In medical science, image processing techniques play a significant function. Computational automation of the treatment is the most authentic and prominent method.  The disease of the brain is identified using Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Many more scan variations of MRI and PET have been executed for the medical diagnosis.  The medical expert needs a solid strain of the computational scan and it’s related for diagnosis.  The current era of computer research is turning towards clinical diagnosis and etiological analysis based on multimodal image processing.  Based on individual medical modalities the accuracy of diseases diagnosis is decreases. Medical community requires a high accuracy in the disease’s identification based on multimodal scan image data.  For the diagnosis and treatment of disorders requires precise information that is attained through various modalities of medical images such as Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI). In image processing the image fusion is the method of merging two images into a single picture.  The obtained single fused image using various multi-modality medical images is enhanced anatomical, highly desirable spectral information compared to the raw single scanned image. This multi – modal fused image is useful for clinical diagnosis of medical experts. In this research work, the system is prepared for the preprocessing of the MRI and PET scan images. The pre-processing techniques enhance the quality of the input images which are degraded and non-readable. For the pre-processing approach we have applied the Gaussian filters of spatial filtering techniques.  The enhanced images passed to the fusion of different region of brain images using Discrete Wavelet Transform (DWT). The system achieved around 90- 95% more accurate outcomes by diluting the color change.  The outcome fused image is achieved without losing the spectral and anatomical data. The experiment has been tested on Alzheimer’s, normal axis, and normal coronal brain disease images dataset. The quantitative and graphical analysis indicates that’s the Discrete Wavelet Transform (DWT) significantly improves the quality of fused images.