# Advances in Computer-Aided Medical Image Processing
## 3 Medical Image Processing  
### Direct Image Processing  
#### 3.1.1 Medical Image Enhancement Technology  
In Section 2, we explain that medical imaging is one of the most important diagnosis and treatment methods in modern hospitals, which is of great importance for tumor detection. However, the existing medical images have problems, such as low brightness, poor contrast, indistinct detail, and a large amount of granular noise. The medical images are dark, the contrast between the target area and the background is low, and there are burrs at the edge of the image. These problems make it difficult and easy to distinguish fine features, which is not conducive to the recognition of observers. Therefore, research on image enhancement of medical images [62] has always been a popular topic in the fields of automatic tumor identification and intelligent medicine. The basic principle of image enhancement is to enhance the contrast and information contained in the image to make it more suitable for specific applications, enhance the detection of the edge of the lesion area, and make the image easier to analyze by humans or computers. As a result of the low brightness and poor contrast of medical images, methods, such as spatial image enhancement, can be well solved. The most commonly used method of spatial image enhancement is the global histogram equalization algorithm (GHE), which is based on the principle that all pixels of the image are uniformly distributed according to the pixel level; that is, the gray distribution of the original image is spread from the more concentrated area to the entire gray range and evenly distributed. This not only can enhance the local contrast without affecting the overall contrast but also can make the  
Figure 4: The top image is taken from FLAIR’s strong magnetic field, and the bottom image is taken from FLAIR’s weak magnetic field in the same position from left to right. Figure 5: The picture on the left is a CT at the regular-dose level, and the picture on the right is a CT at the 1/10-dose level. picture brightness more evenly distributed on the histogram. On the method of global histogram equalization, contrast limiting adaptive histogram equalization (CLAHE) changes the image contrast by calculating the local histogram of the image and reallocating the brightness, increases the contrast limiting for each small region, and overcomes the problem of excessive noise amplification in adaptive histogram equalization. However, the spatial image enhancement method can only enhance the brightness and contrast of the image, and there are still many problems, such as nonuniform noise distributed in the medical image, and some image details will disappear after transformation, which is un-natural overenhancement in areas with high brightness, such as bones. Due to the problem of medical image particle noise and edge burrs, frequency image enhancement can be a good solution. The frequency domain enhancement method is a general term for image enhancement methods in the frequency domain through different filters. Among them, the wavelet transform [63; 64] and curvelet transform [65] are the most common. Wavelet transform is a new transform analysis method based on short-time Fourier transform. It changes the infinite trigonometric function basis into a finite decaying wavelet basis, which makes the time-frequency analysis and signal processing more efficient. The curvelet transform is an improvement based on the Fourier transform and wavelet transform. Compared with the traditional wavelet transform, the azimuth factor is added to obtain a high degree of anisotropy. For shape restoration along the edge and peripheral noise suppression of the main structure, the wavelet transform has unique advantages and can better express the image edge. The above methods can improve the image quality in the single aspect of enhancing contrast or reducing noise. Therefore, many researchers consider merging the two methods. They divide the image into two parts: low frequency and high frequency.