To realize content-hased retrieval of large image databases, it is required to develop an efficient index and retrieval scheme. This paper proposes an index algorithm of clustering called CMA, which supports fast retr...To realize content-hased retrieval of large image databases, it is required to develop an efficient index and retrieval scheme. This paper proposes an index algorithm of clustering called CMA, which supports fast retrieval of large image databases. CMA takes advantages of k-means and self-adaptive algorithms. It is simple and works without any user interactions. There are two main stages in this algorithm. In the first stage, it classifies images in a database into several clusters, and automatically gets the necessary parameters for the next stage-k-means iteration. The CMA algorithm is tested on a large database of more than ten thousand images and compare it with k-means algorithm. Experimental results show that this algorithm is effective in both precision and retrieval time.展开更多
Taking advantage of the new standard HTML5,we designed an online tool called a browser/server-based glaucoma image database builder(BGIDB)for the demarcation of the optic disk and cup’s ellipse-like boundaries.The B-...Taking advantage of the new standard HTML5,we designed an online tool called a browser/server-based glaucoma image database builder(BGIDB)for the demarcation of the optic disk and cup’s ellipse-like boundaries.The B-spline interpolation algorithm is used,and a specially designed algorithm is proposed for classifying the disease grade according to the disc damage likelihood scale criterion,which is correlated strongly with the glaucoma process by quantity.This tool exhibits the best performance with a low overlapping error of 4.34%for the optic disk demarcation and 8.31%for the optic cup demarcation.It also has preferable time-consuming as compared to other tools and is a cross-platform system.This tool has already been utilized in building the ophthalmic image database in the cooperation of Center for Ophthalmic Imaging Research and The Second Xiangya Hospital.展开更多
基金This project was supported by National High Tech Foundation of 863 (2001AA115123)
文摘To realize content-hased retrieval of large image databases, it is required to develop an efficient index and retrieval scheme. This paper proposes an index algorithm of clustering called CMA, which supports fast retrieval of large image databases. CMA takes advantages of k-means and self-adaptive algorithms. It is simple and works without any user interactions. There are two main stages in this algorithm. In the first stage, it classifies images in a database into several clusters, and automatically gets the necessary parameters for the next stage-k-means iteration. The CMA algorithm is tested on a large database of more than ten thousand images and compare it with k-means algorithm. Experimental results show that this algorithm is effective in both precision and retrieval time.
基金Projects(61672542,61573380)supported by the National Natural Science Foundation of ChinaProject(2016zzts055)supported by Fundamental Research Funds for the Central Universities,China
文摘Taking advantage of the new standard HTML5,we designed an online tool called a browser/server-based glaucoma image database builder(BGIDB)for the demarcation of the optic disk and cup’s ellipse-like boundaries.The B-spline interpolation algorithm is used,and a specially designed algorithm is proposed for classifying the disease grade according to the disc damage likelihood scale criterion,which is correlated strongly with the glaucoma process by quantity.This tool exhibits the best performance with a low overlapping error of 4.34%for the optic disk demarcation and 8.31%for the optic cup demarcation.It also has preferable time-consuming as compared to other tools and is a cross-platform system.This tool has already been utilized in building the ophthalmic image database in the cooperation of Center for Ophthalmic Imaging Research and The Second Xiangya Hospital.
