Adaptive Contour Detection base on GVF for Nature and Medical Images

• Main Authors: Tsai-Pan Lin, 林才槃
• Other Authors: I-Cheng Chang
• Format: Others
• Language: en_US
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/74913407317869772991

碩士 === 國立東華大學 === 資訊工程學系 === 94 === Snake, also known as active contour model, is widely used in searching for region of interesting (ROI) in different images. However, Snake has many weak points, and they do deeply affect the range of application, especially in the use of medical images. Many researchers tried to modify Snake algorithm to increase the performance, however, they were hardly to be used widely, because they were only used in specific kinds of medical image. Other than the medical images, people also apply snake to natural images. However, the results from natural images were not successful because Snake is mainly based on gradient vector field to search for ROI, but the gray values of an image cannot efficiently contain color information. Therefore, it was not enough to search for ROI in natural images if we simply use Snake. This thesis takes the gradient vector flow field (GVF) as the base of the proposed system, and then it processes the medical images and natural images separately. The system is mainly divided into two parts. The first is the medical image. Because the histograms of medical images always flock in some specific area, the system uses the technique of image enhancement to raise the contrast of medical images to accentuate the boundary information of medical images efficiently. The second is the natural images. In order to present the color information of natural images efficiently, we firstly use Fuzzy c-means to segment the image, and then combine the results of the segmentations with the traditional gradient information. After using the above methods to deal with images, we use Canny to search for the edge map of the images to substitute the traditional gradient information of GVF. Comparing with the methods that people proposed before, which were mostly aimed at a specific medical image, this paper proposes a method that can be used in the three types of medical images, i.e., CT, MRI, and ultrasound images. As for the process of natural images, we add the information of color to supply the lack of GVF; therefore, we can find the outlines of our objects in the images and broaden the application of the GVF, which originally was used in medical images.