## 3 Application examples of CAD for brain diseases
### Multiple sclerosis  
Multiple sclerosis (MS) is a progressive neurological disorder, which is caused by structural damages of axons and their myelin sheathes in the central nervous system. MS lesions present temporal changes in shape, location, and area between patients, and thus it is necessary for radiologists to accurately detect and evaluate MS lesions [3-6]. However, the accurate assessment of each lesion in \begin{table}
\begin{tabular}{c c c c c c} \hline Authors & Classifier & Feature & No.cases & & \\  & & & (No.AD cases) & Accuracy (\%) & AUC* \\ \hline [PERSON] _et al._[80] & Thresholding & Z-score** & 72 (31) & 87.8 & 0.949 \\ [PERSON] _et al._[81] & SVM*** & Hippocampal & 39 (19) & 89.6 & - \\  & & volume & & \\ [PERSON] _et al._[82] & SVM & Gray matter & 158 (67) & 89.0 & - \\  & & voxels, _etc._ & & & \\ [PERSON] _et al._[83] & Thresholding & Hippocampal & 50 (25) & 84.0 & - \\  & & volume & & & \\  & & Cortical & & & \\  & & thickness, _etc._ & 54 (29) & 82.7 & 0.909 \\ \hline \multicolumn{6}{l}{*AUC : area under receiver-operating characteristic curve} \\ \multicolumn{6}{l}{**Z-score = ([control mean]–[individual value])/(controlSD)} \\ \multicolumn{6}{l}{***SVM : support vector machine} \\ \end{tabular}
\end{table} 
Table 3: Comparison of methods and performance of CAD systems for Alzheimer’s disease using MR images. Figure 13: Segmentations of brain regions (outer white line) and white matter regions (inner white line) obtained by the proposed method from a non-AD patient in multiple coronal planes [27]. magnetic resonance (MR) images would be a demanding and time-consuming task, and also a manual measurement could be subjective and have poor reproducibility. Therefore, a number of semi-automated or automated methods have been proposed for identifying and/or segmenting MS lesions based on two or three-dimensional (3-D) MR images. [PERSON] _et al._[88] proposed an automated approach based on relaxometric and geometric features for classification of MS lesions in 1.5 Tesla 3-D MR images. [PERSON] _et al._[89] applied the FCM algorithm to 1.5 Tesla two-dimensional (2-D) MR images for classifying normal and abnormal brain structures. [PERSON] _et al._[90] proposed an automated method by using an intensity-based tissue classification and a stochastic model for detection of MS lesions in 1.5 Tesla 3-D images. [PERSON] _et al._[91] developed an automated framework for the pipeline analysis of MS lesions in MRI data. [PERSON] _et al._[92, 93] developed an automated method for segmentation of MS lesions in brain MR FLAIR images using an adaptive mixture method and a markov random field model in 1.5 Tesla 3-D MR images. Their proposed approach was based on a Bayesian classifier to obtain and upgrade the class conditional probability density function and a prior probability of each class. Table 4 shows the comparison of methods and results of CAD systems for detection of MS in MR images. The researchers for MS CAD systems are not likely to present the sensitivity and the number of false positives except for our group. Instead, they reported the similarity indices, which range from 0.51 to 0.75. We are developing a CAD system for detection of MS lesions in brain 3.0 Tesla two-dimensional MR images based on a level set method [30, 31]. In the proposed method, the level set method was employed for reduction of false positives as well as accurate segmentation of MS lesions. A number of false positive candidate regions were removed according to the output of an SVM, which was learned with image features and tested based on a leave-one-candidate-out test method. We applied the proposed method to 49 slices selected from 6 studies of three MS cases including 168 MS lesions. As a result, the sensitivity for detection of MS lesions was 81.5 % with 2.9 false positives per slice based on