Abstract
Medical image segmentation is one of the key technologies in computer aided diagnosis. Due to the complexity and diversity of medical images, the wavelet multi-scale analysis is introduced into GVF (gradient vector flow) snake model. The modulus values of each scale and phase angle values are calculated using wavelet transform, and the local maximum points of modulus values, which are the contours of the object edges, are obtained along phase angle direction at each scale. Then, location of the edges of the object and segmentation is implemented by GVF snake model. The experiments on some medical images show that the improved algorithm has small amount of computation, fast convergence and good robustness to noise.
Highlights
Image segmentation is used widely in medical fields, such as medical research, clinical diagnosis and treatment, efficacy evaluation, image information processing, surgical planning, computer-assisted surgery, pathological analysis, image-guided surgery and surgical simulation [1]-[3]
The improved algorithm is based on the deformable model called gradient vector flow method combined with wavelet analysis
The edge points of the interested field are detected by using the wavelet transform, and the Gradient Vector Flow field is constructed on the basis of edge detection results
Summary
Image segmentation is used widely in medical fields, such as medical research, clinical diagnosis and treatment, efficacy evaluation, image information processing, surgical planning, computer-assisted surgery, pathological analysis, image-guided surgery and surgical simulation [1]-[3]. In these applications, the most important and essential aspect is the image segmentation whose application is usually located in a bottleneck situation, the work of medical image segmentation is very difficult, especially to complex image with much noise and the fuzzy boundary images. The improved algorithm is based on the deformable model called gradient vector flow method combined with wavelet analysis
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