## 3 Application examples of CAD for brain diseases
### White matter hyperintensities in vascular dementia  
Subcortical vascular dementia (VaD) is a neurodegenerative vascular disorder that leads to a progressive decline in memory and cognitive functioning, and is considered the second most common cause of dementia in Japan. The prevalence of the VaD in people older than 70 years in Japan is around 27% including mix dementia of Alzheimer's disease and VaD [67]. Ischemic lesions in VaD \begin{table}
\begin{tabular}{c c c c c c} \hline Authors & \begin{tabular}{c} Lesion \\ enhancement \\ \end{tabular} & Classifier & \begin{tabular}{c} No.cases \\ (No.aneurysms) \\ \end{tabular} & \
\begin{tabular}{c} Sensitivity \\ (\%) \\ \end{tabular} &
\begin{tabular}{c} No.FPs per \\ case \\ \end{tabular} \\ \hline [PERSON] _et al._[66] & Fuzzy degree & Fuzzy & 16 (19) & 100 & 6.4 \\ [PERSON] _et al._[41] & Curvature & – & 18 (23) & – & – \\  & Gradient & & & & \\ [PERSON] _et al._[65] & concentrate & QDA* & 7 (7) & 100 & 1.9 \\  & filter & & & & \\ [PERSON] _et al._[26] & Hessian matrix & LDA** & 115 (61) & 97 & 3.8 \\ \hline \multicolumn{6}{l}{*QDA: quadratic discriminant analysis} \\ \multicolumn{6}{l}{**LDA: linear discriminant analysis} \\ \end{tabular}
\end{table} 
Table 1: Comparison of methods and performance of CAD systems for detection of intracranial aneurysms using MRA. Figure 11: Illustrations of images produced in each step of the shape-based difference image technique: (a) segmented vessel, (b) distance transformation, (c) determination of vessel skeleton, (d) logical AND between skeleton image and distance transformed image, (e) smoothing skeleton image with distance value by rind filter, and (f) vessel with suppressed local change on thickness [26]. patients show hyperintense regions in the cerebral white matter, _i.e._, periventricular hyperintense (PVH) regions and white matter hyperintense (WMH) regions, on FLAIR images or T2-weighted images at MR imaging. It has been reported that the degree of the symptom of the VaD correlated with an area ratio of WMH region to the whole brain region (WMH region ratio) [68-71], which could provide diagnostic information for the treatment of VaD. Thus, it is very important to estimate the WMH region ratio for evaluating the degree of the VaD. Although neuroradiologists attempt to manually evaluate the WMH regions, it is very laborious and demanding in routine clinical practice. Therefore, investigators have reported CAD systems for detection of WMH including lacunar infarct using MR images [28, 72-76]. [PERSON] _et al._[72] developed a segmentation method for white-matter lesions including VaD, multiple sclerosis, and lacunar infarcts based on a modified k-mean-neighbor method using three-dimensional (3D) MR images. [PERSON] _et al._[73] presented a method for automated segmentation of WMHs in MR images by using a k-nearest neighbor method, which builds a feature space from voxel intensity features and spatial information. [PERSON] _et al._[74] proposed a automatic segmentation method of WMHs based on a fuzzy C-mean algorithm using three different MR images: proton density, T2-weighted, and FLAIR images. [PERSON] _et al._[75] developed a computerized scheme for detection of lacunar infarct by using a rule-based method and an SVM. Their method detected 96.8% of lacunar infarct with 0.76 false positives. [PERSON] _et al._[76] suggested an approach for segmentation of white-matter lesions related to cerebrovascular disease based on a SVM on a 3D MR image. On the other hand, for assisting neuroradiologists in the diagnosis of VaD, [PERSON] _et al._[28] developed an automated method for detecting and segmenting WMH regions based on conventional MR images (T1-weighted, T2-weighted, and FLAIR images). Their method attained a sensitivity of 90% with 4.0 false-positives per slice in detection of WMH regions. Table 2 shows a comparison of methods and performance of CAD systems for segmentation of WMH using MR images.