Abstract
Brain diseases such as degenerative (alzheimer's disease), neoplastic disease (brain tumor like sarcoma, glioma) are considered an interesting topic areas in the medical image fusion diagnosis. Pixel-level image fusion techniques are designed to combine multiple/multi-scale input images into a fused image, which is expected to be more informative for human or machine perception as compared to any of the input images. Since they are difficult to be summarized ; survey paper are characterized by (1) medical image definition , brain diseases challenges , analysis a various techniques for multi-scale image fusion with its own modalities, fusion rule, fusion strategy and dis-advantage ,Whilst used a database of medical images for medical Harvard School (brain diseases) which contains various groups of co-registered multi-modal images including MRI/CT, MRI/PET and PET/SPECT and MRI (T1/T2) images.
Highlights
Over the past several decades’ diseases have fallen before the scythe of human intelligence in the form of biomedical advances
Journal of Biomedical Engineering and Medical Imaging, Volume 7, No 1, February (2020), pp 18-38 features and minimize randomness and redundancy for maximize the clinical applicability of images for diagnosis and assessment of medical issues .Today, medical image fusion was consider a main solution to overcome medical issues reflected through images of human body, organs, and cells since it contains a various range teqinques medical images fusion and information fusion .Medical images modalities mainly concerned on Ultrasound Guided Imaging (USG), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) along with functional MRI, Positron Emission Tomography (PET), and Single-Photon Emission Computed Tomography (SPECT) [3,4,5,6,7,8,9,10,11,12] as illustrated in figure
These sensors support us complementary information about patient’s pathology, anatomy, and physiology and specially for brain disease, For example, CT is widely used for tumor and anatomical detection, whereas information about soft tissues is obtained by MRI
Summary
Over the past several decades’ diseases have fallen before the scythe of human intelligence in the form of biomedical advances. Journal of Biomedical Engineering and Medical Imaging, Volume 7, No 1, February (2020), pp 18-38 features and minimize randomness and redundancy for maximize the clinical applicability of images for diagnosis and assessment of medical issues .Today, medical image fusion was consider a main solution to overcome medical issues reflected through images of human body, organs, and cells since it contains a various range teqinques medical images fusion and information fusion .Medical images modalities mainly concerned on Ultrasound Guided Imaging (USG), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) along with functional MRI (fMRI), Positron Emission Tomography (PET), and Single-Photon Emission Computed Tomography (SPECT) [3,4,5,6,7,8,9,10,11,12] as illustrated in figure.1 These sensors support us complementary information about patient’s pathology, anatomy, and physiology and specially for brain disease, For example, CT is widely used for tumor and anatomical detection, whereas information about soft tissues is obtained by MRI. T1-MRI image provides details about anatomical structure of tissues, whereas T2-MRI image gives information about normal and abnormal tissues [13]
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