A Review on Brain Tumor Detection Using Single and Multi Modal Image Fusion Techniques
In human brain, the abnormal process like uncontrolled growth and multiplication of tissues may occur. This kind of unhealthy growth of mass in human brain is termed as “brain tumor”. This paper presents review of different techniques on brain tumor detection and classification. The brain tumor can be detected and analyzed by using various image processing techniques like brain Magnetic Resonance Imaging (MRI),, Electroencephalography (EEG), Computed Tomography (CT), Positron Emission Tomography (PET), Biopsy, Magneto Encephalography (MEG), Single Photon Emission Computed Tomography (SPECT), etc. Each imaging technique has its own advantages and practical demerits. In comparison to these modalities, MRI is the most extensively preferred technique because of its safe and reliability. In addition to these single imaging techniques, there are a large number of research scopes and advancement in the field of multi modal image fusion. Image fusion method combines different sources of information from various modalities and produce a single image which is useful for human perception and machine vision. From the review of existing research papers, the whole process of detecting brain tumor using image fusion method, are classified into five broad categories: image preprocessing, image decomposition and its reconstruction, image fusion principles and parameters of image quality metrics, feature extraction, image segmentation and classification. Here authors present a view that there is a need for research on brain image fusion technique and efficient brain tumor detection and classification. Finally paper presents the futuristic scope to overcome the research gap.