Brain Tumor Segmentation Study Based On Hough Transform Location And Genetic Algorithm

Brain tumor is a disease with high morbidity and mortality.The precise segmentation of the tumor area in the brain images is critical important for the subsequent diagnoses and treatments.With the advancement of medical imaging technology and the development of computer technology,Computer-aided diagnosis technology attracts more and more attention.Using computer to assist in segmentation of medical images can improve accuracy of segmentation results and doctor's work efficiency.According to brain tumor image segmentation problems and tumor shape characteristics,this paper proposes an automatic brain tumor segmentation method,which bases on Hough transform location and improves genetic algorithm to optimize the GVF Snake model.The main work is as follows:1?According to the characteristics of the magnetic resonance imaging(MRI)of brain tissue and the need for brain tumors automatic location and segmentation,brain tissue MRI is preprocessed by anisotropic filtering,edge detection,and cranial dissection to remove non-brain components such as skull,scalp,and blood vessels.The process improves the contrast of gray areas in the tumor area.This lays the foundation for the extraction of tumor regions from multiple arched contour regions detected by Hough transform.2?Based on the shape features of the tumor is a curved outline,the method of circle detection based on Hough transform is used to detect all the regions with arc contour in the edge image of preprocessed brain tissue.And according to the characteristics of brain tumors with a high pixel gray value,the gray threshold processing is applied to extract the tumor area from the arc region to complete locating the tumor areas.The initial contour is determined according to the location results,and the GVF Snake model is applied to coarse segmentation of the tumor.The experimental results show that the proposed tumor localization method can accurately locate the tumor area automatically with no manual intervention,improves the efficiency of the doctor's work and avoids segmentation results subject to the subjectivity of manual operations.3?the traditional genetic algorithm uses the contour curve control points as the coding object,so there will possible be a problem appeared that the curve control points are prone to jump in the weak boundary area.In order to solve the problem that a new segment-based crossover and mutation operator is designed.To address the problem that the GVF Snake model is easily trapped into local minimum,the results of GVF Snake coarse segmentation are further optimized by using improved genetic algorithm.The experimental results show that the improved crossover and mutation operator of genetic algorithm is used to optimize the coarse segmentation results of GVF Snake model,in the case of the smoothness of the segmentation curve.It can also avoid the local convergence problems of GVF Snake model,and further improve the accuracy of the segmentation result.