Enhancement of Hybrid Multimodal Medical Image Fusion Techniques for Clinical Disease Analysis

Enhancement of Hybrid Multimodal Medical Image Fusion Techniques for Clinical Disease Analysis

Rajalingam B. (Annamalai University, Chidambaram, India) and Priya R. (Annamalai University, Chidambaram, India)
Copyright: © 2018 |Pages: 25
DOI: 10.4018/IJCVIP.2018070102

Abstract

Multimodal medical image fusion is one the most significant and useful disease analytic techniques. This research article proposes the hybrid multimodality medical image fusion methods and discusses the most essential advantages and disadvantages of these methods. The hybrid multimodal medical image fusion algorithms are used to improve the quality of fused multimodality medical image. Magnetic resonance imaging, positron emission tomography, and single photon emission computed tomography are the input multimodal therapeutic images used for fusion process. An experimental result of proposed hybrid fusion techniques provides the fused multimodal medical images of highest quality, shortest processing time, and best visualization. Both traditional and hybrid multimodal medical image fusion algorithms are evaluated using several quality metrics. Compared with existing techniques the proposed result gives the better processing performance in both qualitative and quantitative evaluation criteria. This is favorable, especially for helping in accurate clinical disease analysis.
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1. Introduction

The continuous development of medical imaging and information processing technologies provides various types of multimodality medical images for clinical disease analysis. The medical images are mostly used in disease analysis, treatment centre, and radiation treatment. However, the obtained sensor responses of different modalities of medical images express different information about the human body, organs, and cells, and have their personal utilize. Image fusion is the combination of two or more different images to form a new image by using certain techniques. It is extracting information from multi-source images and improves the spatial resolution for the original image and preserves the spectral information. Image fusion can be classified into three levels Pixel level fusion, feature level fusion and decision level fusion. Pixel-level fusion preserves the salient information of the fused image. Feature-level fusion performs on feature-by-feature basis, such as edges, textures. Decision-level fusion refers to make a final fused decision. The image fusion reduces the storage and holds the essential information. It creates new image that is more suitable for the purposes of human/machine perception or for further processing tasks. Image fusion is classified into two types: single sensor and multi sensor. Single sensor captures the real world as a sequence of images. Ex: Multi focus and Multi Exposure fusion. Multi sensor image is merging the images from several sensors to form a composite image. Ex: medical imaging, military area.

Multimodality medical images categorized into several types which include computed tomography (CT), magnetic resonance angiography (MRA), magnetic resonance imaging (MRI), positron emission tomography (PET), ultra-sonography (USG), nuclear magnetic resonance(NMR) spectroscopy, single photon emission computed tomography (SPECT), X-rays, visible, infrared and ultraviolet. MRI, CT, USG and MRA images are the structural medical images which give high quality images. PET, SPECT and functional MRI (fMRI) images are functional therapeutic images which give low-spatial resolution images with functional information. Anatomical and functional therapeutic images can be incorporated to obtain more constructive information about the same object. Medical image fusion reduces storage cost by storing the single fused image instead of multiple-input images. Multimodal medical image fusion uses the pixel level fusion. Different imaging modalities can only provide limited information. Computed Tomography image can display accurate bone structures. Magnetic Resonance Imaging image can expose regular and pathological soft tissues.

The fusion of CT and MRI images can integrate complementary information to minimize redundancy and improve diagnostic accuracy. Combined PET/MRI imaging can extract both functional information and structural information for clinical diagnosis and treatment. The fused multimodal medical image not only obtains a more accurate and complete description of a target, but also reduces randomness and redundancies produced by the sensor in the medical image. Multimodal medical Image fusion increases the effectiveness of image-guided disease analysis, diagnoses and the assessment of medical problems. Image fusion having several applications like medical imaging, biometrics, automatic change detection, machine vision, navigation aid, military applications, remote sensing, digital imaging, aerial and satellite imaging, robot vision, multi focus imaging, microscopic imaging, digital photography and concealed weapon detection. Multimodal medical imaging plays a vital role in a large number of healthcare applications including medical diagnosis and treatment. Medical image fusion methods involve the fields of image processing, computer vision, pattern recognition, machine learning and artificial intelligence.

The research paper is organized as follows. Section 2 describes the literature survey on related works. Section 3 discusses the proposed research work method both traditional and hybrid multimodal medical image fusion techniques. Section 4 performance evaluation metrics is briefly reviewed and describes the implemented multimodal medical image fusion experimental results and performance comparative analysis. Finally, Section 5 concludes the paper.

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