# Advances in Computer-Aided Medical Image Processing
## 3 Medical Image Processing
### Intelligent Image Recognition  
2.1 Image Processing Methods According to the Image Characteristics of Each Medical Part of the Body  
In Section 2, we summarize the current problems in medical imaging. In this section, we summarize the current problems in medical imaging and the characteristics of the brain, breast, liver, and lung that affect imaging and review the image processing methods proposed for the characteristics of these parts, as well as the effects achieved.  
Faced with the problems of unclear edges, slender structure, structural changes along pathways, and low contrast with neighboring anatomical structures in the general details of brain tumors, [PERSON] et al. [62] proposed a learning method based on local shape and sparse appearance by taking advantage of the stable characteristics of the general structure of the brain. Solving the problem of unclear edges through a fast and robust shape localization method using conditional space deep learning used an optimized zonal statistical shape and appearance model based on regional shape changes to more flexibly adapt to long and thin brain structures. MRI sequences from 165 child subjects were evaluated, and the DICE similarity coefficient was 0.779. [PERSON] et al. [27] used [PERSON]'s multilayer perceptron algorithm in the CNN model to learn both nonlinear and linear models and evaluated the CNN model by using the confusion matrix, achieving 86.63% accuracy in [PERSON]'s dataset. [PERSON] et al. [22] applied VGG-19 in the series with hand-marked features, and the DSC test results on BRATS 2015, BRATS 2016, and BRATS 2017 were 0.99, 1.00, and 0.99, respectively.  
Faced with the problem that liver tumors have overlapping regions and unclear edge imaging, the boundary of tumor regions is uncertain during segmentation. [PERSON] et al. [66] proposed an entropy-based fuzzy region growth method to address the segmentation of multiple tumors in the same CT image between overlapping tumors in liver CT images. The mean values of AOE, RAD, and DSM obtained on the ImageCLEF dataset were 19.9, 15.45, and 0.88, respectively. [PERSON] [53] proposed a CT liver image segmentation algorithm based on fuzzy C-means (FCM) and random walk to delineate CT liver tumor boundaries. In MIDAS database image segmentation, an average DSC of 0.81, OE of 15.61%, and RD of 4.02% can be obtained based on this research algorithm.  
Faced with the problem of breast tumor structure distortion and difficulty in recognition and feature extraction, [PERSON] et al. [36] proposed an enhanced workflow based on the CNN model, a CNN modelling method based on explainable context, which intelligently selected disease-related input images for training and modelling. The final results were generated using CNN tag predictions and an observable qualitative tag signature for each new image, with an accuracy of 82% using the auxiliary dataset provided by the Tumor Proliferation Assessment Challenge 2016 (TUPAC 2016). [PERSON] et al. [11] adopted the Inception-ResNet-V2 deep convolutional neural network model to realize the eight classifications of pathological images of breast cancer and adopted the BreaKHis dataset to achieve a tumor recognition rate of 82.6%.  
Faced with the uncertainty of possible sites of lung cancer, including the chest wall, airway, pulmonary fissure, or blood vessels, clinical diagnosis is therefore a complex task. Taking advantage of the fact that the strength surface of malignant nodules is coarser than that of benign nodules, [PERSON] et al. [67] proposed a set of fractal features based on the fractional Browne motion model to automatically distinguish malignant nodules from benign nodules. In a group of 107 CT images from 107 different patients, the results were tested with an accuracy rate of 88.82%. [PERSON] et al. [68] used YOLO v3 and Google Inception v3 network training to form end-to-end detection. H-E staining pathological sections of lung lesions of 952 patients were used to assist doctors in identifying 100% vs. 95.52% independently.