Application of Improved SVM Image Segmentation Algorithm in Computer Tomography Image Analysis
Medical imaging is becoming increasingly important in clinical diagnosis. Ultrasound imaging, computed tomography, magnetic resonance imaging (MRI) and other new medical imaging technology greatly broadens the imaging diagnostic methods. Animal Computer Tomography (CT) imaging, as an animal model, i...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Bulgarian Academy of Sciences
2017-03-01
|
| Series: | International Journal Bioautomation |
| Subjects: | |
| Online Access: | http://www.biomed.bas.bg/bioautomation/2017/vol_21.1/files/21.1_05.pdf |
| Summary: | Medical imaging is becoming increasingly important in clinical diagnosis. Ultrasound imaging, computed tomography, magnetic resonance imaging (MRI) and other new medical imaging technology greatly broadens the imaging diagnostic methods. Animal Computer Tomography (CT) imaging, as an animal model, is of great significance to guide the experimental research of clinical diagnosis, and the treatment of pet disease also has a pioneering significance. Image segmentation, as the basis of medical image processing and analysis, has played a vital role in clinical diagnosis and treatment from doctors. In this paper, the existing segmentation algorithm is improved based on the characteristics of CT images of animals. In this paper, we use the global optimization of the genetic algorithm to improve the traditional support vector machine classification algorithm. At the same time, the kernel function of the support vector machine algorithm is improved to promote the segmentation results. The experiments show that the algorithm in this paper has a better segmentation effect in the processing of CT images of animals. |
|---|---|
| ISSN: | 1314-1902 1314-2321 |