Automatic Classification Software for MRI brain scans: A Diagnostic tool

Lead Research Organisation: University of Sussex
Department Name: Sch of Mathematical & Physical Sciences


A major challenge facing humankind is an aging global population and the associated increase in degenerating and debilitating diseases, such as Alzheimer's disease (AD). Early diagnosis is essential to improve patient quality of life and minimize social costs. Magnetic Resonance Imaging (MRI) is a well-established tool for studying brain abnormalities. Conventional MRI can only reveal unspecific brain atrophy in AD, but it has been demonstrated that "quantitative" MRI techniques, together with functional MRI (fMRI) can provide signatures of the onset of dementia (Bozzali et al., 2011). Resting-state fMRI is a relatively novel approach to detect spontaneous brain activity at rest (Greicius et al., 2004). Resting-state fMRI is potentially very powerful for the early diagnosis of dementia (Zhou et al., 2010). However, it is regarded as unsuitable for clinical use owing to the volume of data, the complex image analysis required, and the difficulty in the interpretation of results.

A number of functional networks in the brain have been identified using Independent Component Analysis (ICA) deconstructions of resting-state fMRI data. However, the interpretation of the raw images and even the compressed ICA data requires an experienced human eye. In particular, once the networks have been identified, they are typically analysed separately using univariate statistical approaches. A multivariate approach providing a quick, automatic, objective classification and diagnosis of images would have a huge impact in clinical and research arenas.

Our proposal is to illustrate proof of concept for the automatic classification of MRI imaging of the brain. We will use existing ICA decomposition of patient and control groups in resting conditions. Our plan is to use a machine learning technique, specifically a Bayesian classifier using Gaussian mixtures, which has been used in Astronomical research. We will code a prototype diagnostic tool, based on resting state fMRI, which can be tested by the CISC group. Once validated, this approach can be easily extended to the joint analysis of other MRI modalities.

This proposal brings together the Astronomy Centre and the Clinical Imaging Science Centre (CISC) at the University of Sussex. The Astronomy Centre brings extensive expertise in statistical analysis and software development while the CISC brings extensive human-classified data sets and deep understanding of the clinical problems and expertise in resting state fMRI and other MRI techniques.

Planned Impact

1. Altzheimer's disease patients: objective and early diagnosis of Altzheimer's disease which would have a significant benefit on the quality of life of patients
2. The World Health Organisation recently announced for world health day (April 2012) that it "is calling for urgent action to ensure that, at a time when the world's population is ageing rapidly, people reach old age in the best possible health".
3. Pharmaceutical Research and Manufacture: Early and objective, quantitative diagnosis would also have an impact on drug development, trials and sales.
4. Clinical Technology Industries: e.g. Elekta which could apply early diagnosis into procedures for intervention
5. Clinical Practitioners: e.g. Brighton and Sussex University Hospital
6. NHS and department for health: early diagnosis would have implications for public health policy and practice


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Description We have developed a new technique for classifying patients into classes of no condition / mild cognitive impairment and / Alzheimer's on the basis of fMRI imaging and other data. We used a Bayesian probabilistic machine learning technique Gaussian Logistic Regression.
Exploitation Route Some might adopt this explicit technique for clinical use in this particular application. Others might adapt the technique for other sorts of data e.g. providing diagnosis of different conditions from other sorts of data. The data does not need to be images it can be a mix of different data types.
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