Ms. Jingnan Wang Profile

Jingnan Wang

at Philips Research

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Proceedings Article | 27 March 2009 Paper

Voxel-based discriminant map classification on brain ventricles for Alzheimer's disease

Jingnan Wang, Gerard de Haan, Devrim Unay, Octavian Soldea, Ahmet Ekin

Proceedings Volume 7259, 72591W (2009) https://doi.org/10.1117/12.810908

KEYWORDS: Brain, Brain mapping, Alzheimer's disease, Control systems, Magnetic resonance imaging, Shape analysis, Dementia, Image segmentation, Neurons, Quantitative analysis

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One major hallmark of the Alzheimer's disease (AD) is the loss of neurons in the brain. In many cases, medical experts use magnetic resonance imaging (MRI) to qualitatively measure the neuronal loss by the shrinkage or enlargement of the structures-of-interest. Brain ventricle is one of the popular choices. It is easily detectable in clinical MR images due to the high contrast of the cerebro-spinal fluid (CSF) with the rest of the parenchyma. Moreover, atrophy in any periventricular structure will directly lead to ventricle enlargement. For quantitative analysis, volume is the common choice. However, volume is a gross measure and it cannot capture the entire complexity of the anatomical shape. Since most existing shape descriptors are complex and difficult-to-reproduce, more straightforward and robust ways to extract ventricle shape features are preferred in the diagnosis. In this paper, a novel ventricle shape based classification method for Alzheimer's disease has been proposed. Training process is carried out to generate two probability maps for two training classes: healthy controls (HC) and AD patients. By subtracting the HC probability map from the AD probability map, we get a 3D ventricle discriminant map. Then a matching coefficient has been calculated between each training subject and the discriminant map. An adjustable cut-off point of the matching coefficients has been drawn for the two classes. Generally, the higher the cut-off point that has been drawn, the higher specificity can be achieved. However, it will result in relatively lower sensitivity and vice versa. The benchmarked results against volume based classification show that the area under the ROC curves for our proposed method is as high as 0.86 compared with only 0.71 for volume based classification method.

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