## 2 Basics of CAD systems
### Feature extraction
#### 2.3.1 Image enhancement of an object  
If the gray-level spatial distribution within the object is known, the enhancement filter could be developed. For example, small intracranial aneurysms, which are imaged by MR scanners, could be enhanced by using an enhancement filter for a 3D Gaussian shape, because the shapes of the aneurysms are similar to 3D Gaussian function. Therefore, several enhancement filters for specific lesions have been developed in the CAD field [36-42]. [PERSON] _et al._ developed a difference image technique for the detection of lung nodules in radiographs, based on the subtraction of the object-suppressed image using a ring filter from the object-enhanced image using an object-matched filter [36,43]. [PERSON] _et al._ applied the difference image technique to the automated detection of lung nodules in low-dose CT [44]. [PERSON] _et al._ developed a morphological filter (Quoit filter) to extract the isolated nodules, and they applied the filter to automatic detection of lung nodules in CT images [37]. [PERSON] _et al._ proposed a three-dimensional (3D) filters based on a combination of the eigenvalues of the Hessian matrix of the 3D image for enhancement of specific 3D structures, which are line-like, sheet-like, and blob-like structures corresponding to items such as cerebral vessels, vessel walls, and aneurysms, respectively [38,39]. By using the same theory as [PERSON] _et al._, [PERSON] _et al._ developed selective enhancement filters for dot (=blob), line, and plane (=sheet) objects [40]. In the neuroradiological CAD field, [PERSON] _et al._ applied selective enhancement filters for detection of unruptured intracranial aneurysms in MRA images [25, 26]. Figure 4 shows the effect of a dot-enhancement filter for an MRA image with an unruptured intracranial aneurysm. The balloon-like aneurysms are well enhanced by the dot-enhancement filter. [PERSON] _et al._ proposed a curvature-based  
Figure 3: Scheme of feature extraction in a CAD system.  
enhanced display system for detecting cerebral aneurysms in MR angiography [41]. [PERSON] and [PERSON] _et al._ proposed a simple method for enhancing MS lesions in 2D fluid-attenuated-inversion-recovery (FLAIR) images by subtracting the background image approximated by the first order polynomial in a brain parenchymal region [30, 31]. Figure 5 shows the effect of the background subtraction image on enhancement of MS lesions. Relatively low-contrast MS lesions are well enhanced by suppressing the background in the brain parenchyma. However, one of the problems caused by such kinds of enhancement filters is that the filter induces enhancement of the lesion-like normal structures as well. Consequently, we have to reduce numerous false positives by obtaining a number of image features of the candidate regions enhanced by the filter. To overcome the issue, [PERSON] _et al._ attempted to develop a supervised filter for enhancement of lesions by use of a massive-training artificial neural network (MTANN) in a CAD scheme for detection of lung nodules in CT [42]. The MTANN filter is trained with actual lesions in CT images to enhance the actual patterns of the lesions, not lesion-like normal structures. This supervised filter could be promising for developing CAD systems in the neuroradiological field.  
Figure 4: Effect of a dot-enhancement filter for a MRA image with an unruptured intracranial aneurysm: **(a)** an original MRA image and **(b)** a dot-enhanced image [25, 26].  
Figure 5: Illustrations of (a) an original FLAIR image, and (b) its corresponding subtraction image between the FLAIR image and a background image approximated by the first order polynomial in a brain region [30, 31].