ROI Algorithm For Image Segmentation Based On Region Growing And K-means

In recent years, with the rapid development of modern science and technology and related theory, people’s requirements for digital image processing of the increasing. In the analysis and application of image, people often only on the object which interested, and these objects are often located in different regions in an image,to the detection and measurement of characteristic parameters of the object in the image, we must first the image is divided into some "meaningful" area according to certain requirements, this is the image division of work to be done. Here the "meaningful" refers to these areas. Therefore, a key step in image segmentation for image analysis and understanding, the results will directly affect the target feature extraction and description, and target recognition, further classification and image understanding.This thesis is a study of the ROI algorithm for image segmentation based on region growing and K-Means, the algorithm uses an accurate segmentation algorithm based on aggregation, core idea is the first by the image based on the brightness is divided into several types of presupposition, which can extract the image region of interest(ROI) accurately. Then, by using adaptive threshold algorithm, According to the characteristics of target and background variance region takes the maximum value is the optimal threshold of T image, the completion of image ROI threshold can extract. Finally, using the region growing method to pixels with same feature connected together, thus forming the final segmented regions.This topic using ROI image K-Means and region growing method based segmentation algorithm, using the local spatial information of image, can effectively overcome the disadvantages of image segmentation is not continuous space existing in other methods, can improve the speed of image segmentation is very good, and overcomes the defect of the traditional algorithm’s time complexity and space complexity.