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http://hdl.handle.net/10603/458455
Title: | Multimodal medical image fusion techniques for the early detection of brain disorders |
Researcher: | Reena Benjamin J |
Guide(s): | Jayasree T |
Keywords: | Medical Image Fusion Brain Disorders Neural Networks |
University: | Anna University |
Completed Date: | 2021 |
Abstract: | Image fusion is the process of merging significant information from two or more images into a single image so that the fused image is more accurate and informative than any one of the input source images. Image fusion finds its application in different fields such as remote sensing, medical imaging, surveillance, biometrics, military and astronomy. Nowadays, medical imaging plays a vital role in various healthcare applications including diagnosis of diseases, treatment planning etc. Medical image fusion is the process of combining images from different imaging modalities to enhance the image quality and information to increase the clinical applicability of diagnosing diseases and thus go for surgical planning in a short period of time. newlineGenerally, medical imaging modalities carry complementary information of the human organs and tissues which guide the radiologists and doctors to identify the diseases easily. The most complex human organ is the brain. The brain is responsible for our actions, feeling, memory, thought and experience of the world. The various disorders related to the brain are Alzheimer s disease, brain tumour, stroke, motor neuron disease etc. The various brain imaging techniques allow physicians to study brain activity and the problems related to the brain without invasive neurosurgery. Some of them are Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT). CT and MRI are called anatomical images since they provide only structural information. CT images show the information of bone structures, whereas MRI images give details of soft delicate tissues. newline |
Pagination: | xxv,201p. |
URI: | http://hdl.handle.net/10603/458455 |
Appears in Departments: | Faculty of Information and Communication Engineering |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 20 kB | Adobe PDF | View/Open |
02_prelim_pages.pdf | 2.1 MB | Adobe PDF | View/Open | |
03_contents.pdf | 145.76 kB | Adobe PDF | View/Open | |
04_abstracts.pdf | 124.78 kB | Adobe PDF | View/Open | |
05_chapter1.pdf | 741.42 kB | Adobe PDF | View/Open | |
06_chapter2.pdf | 1.07 MB | Adobe PDF | View/Open | |
07_chapter3.pdf | 3.75 MB | Adobe PDF | View/Open | |
08_chapter4.pdf | 1.2 MB | Adobe PDF | View/Open | |
09_chapter5.pdf | 4.89 MB | Adobe PDF | View/Open | |
10_chapter6.pdf | 2.36 MB | Adobe PDF | View/Open | |
11_annexures.pdf | 261.32 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 128.98 kB | Adobe PDF | View/Open |
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