Investigation and implementation of image processing algorithms for medical applications
Functional Magnetic Resonance Imaging (fMRI) is a relatively new form of neuroimaging techniques that measures haemodynamics response related to brain activity. This new ability to directly observe brain function opens an array of new opportunities to advance our understanding of brain organization,...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | Final Year Project |
| اللغة: | English |
| منشور في: |
2009
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/10356/18048 |
| الوسوم: |
إضافة وسم
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| الملخص: | Functional Magnetic Resonance Imaging (fMRI) is a relatively new form of neuroimaging techniques that measures haemodynamics response related to brain activity. This new ability to directly observe brain function opens an array of new opportunities to advance our understanding of brain organization, as well as a potential new standard for assessing neurological status and neurosurgical risk. However there are an indefinite ways of analyzing fMRI data. It could be divided into 2 main methods in general the model based methods and model free methods which generally uses Exploratory Data Analysis. In Exploratory Data Analysis we would be looking at the clustering branch of analysis which includes Fuzzy C Means, Crisp Clustering and Hierarchical analysis. The algorithms are implemented on real auditory data sets and synthetic datasets to compare the clustering performance. |
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