| Summary: | 博士 === 國立臺灣大學 === 電機工程學研究所 === 102 === This study aimed to build binary methods to extract efficient information from structural brain magnetic resonance (MR) images and functional brain activities. In the era of big data, to collect and analyze all the brain images in hospitals all over the world is technologically possible and might be achieved in the near future. Therefore, simple and effective methods for machine learning algorithms to extract sufficient information from various brain MR images to build classification or regression models based on numerous brain images are critical.
In this study, we used binary methods to extract information from three different types of brain MR images. First, we implemented local binary patterns (LBP) to describe anatomical brain morphology and used those patterns to train support vector machine models to classify the attention deficit-hyperactivity disorder (ADHD) subjects from normal ones. As a result, the best accuracy we achieved was 0.6995. Second, different from the traditional methods, which all brain images should be normalized to a standard template to be compared in same atlas coordinates, the LBP was used to extract information from unnormalized brain anatomical images and diffusion tensor imaging. We then constructed age estimation models by that extracted information to show the discriminative power of this approach. The best test result mean absolute error of that model equals 5.62 years. Third, following the same line of thought, a binary mapping method was designed and introduced to detect schizophrenia and ADHD patients using resting-state functional MRI data. Compared with traditional cross-correlation network analysis, proposed models exhibits better performance in detecting schizophrenia and ADHD. Based on our results, the best test accuracy of discriminating schizophrenia from normal subjects was 0.78. The best test accuracy or classifying ADHD from control subjects was 0.628.
Results showed those simple binary methods are useful for extract information from structural and functional brain MR images. Those methods are good candidates to be used in large-scale brain science or medicine related researches.
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