| Summary: | 碩士 === 國立交通大學 === 資訊科學與工程研究所 === 95 === Recently, the study of computer-aided diagnosis (CAD) becomes a trend of biomedical signal processing due to developments from medical image analysis technology. In the past, a diagnosis depends on doctors' judgments, is subjective to physicians and costs much time for subjects to get results. Moreover, subtle differences which reveal potential danger may be invisible to human eyes. Thus, a simple CAD system with high correct accuracy can supply an index sign for physicians and subjects in an objective and convenient way. Most of existent systems, however, provide an absolute prediction on a test subject. It means that the answer would be either yes or no. Therefore, we propose a probabilistic approach to tell doctors and test subjects probabilistic predictions which show the difference of degree.
In this thesis, we construct a computer-aided MRI evaluation system with statistical pattern recognition technology. The entire system is parallelly composed of several disease classification models and each classification model is aimed at classifying a particular disease. For each model, there are two processes: feature selection and extraction, and classification. Initially, locations where reveal significant anatomical discrepancy discovered by a voxel-based morphometric analysis (VBM) are picked out as distinguishable features for classification. Moreover, principal component analysis (PCA) is applied to find proper representations for those found features and some applicable PCs are chosen to establish a good classification space by two principal component (PC) selection methods. One is named as variance-based PC selection method and the other is significant-based PC selection method. Finally, the classification model predicts the possibility of a test subject to sicken with a particular disease by using Bayes' Theorem and a nonparametric density estimation, Parzen windows.
Our proposed classification framework was applied on spinocerebellar ataxia type III (SCA3) and bipolar disorder (BD) and two corresponding classification models were established separately. Both of two PC selection methods were used in each model. Thus, there were two distinct classifiers in a model. In our experiments, we found that a classifier with significant-based PC selection method not only achieves a better performance but also has a more consistent result.
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