Research On Medical Image Recognition Based On Density Clustering And Multi-Feature Fuse

Image recognition is such a process of classifying images on the features of image by recognition theory and methods,which belongs to the scope of image processing and pattern recognition.Medical image recognition is the vital content of automatic diagnosis by medical images and also an important research direction for medical fields all over the world.A lot of feature information and rules are hiddened in the human medical images.Those images have characteristics of high resolution, huge volumes of data and complicated expression of image features,therefor many challenges must be faced with to recognize them.It is realistically significant to investigate and explore theories and practiccal techniques,methods and algorithms to recognize medical images automatically.This has great practical value for assisting doctors to make clinical diagnosis.Medical images are the objects for studying in this paper On the basis of summary about research works for main hardships,techniques,methods,state of the art and developments of image recognition,this paper presents algorithms,methods and techniques of medical image recognition based on density clustering, multi-feature fuse,fused feature association rule,which can deal with the characteristics of high dimension and complicacy for medical images.The main contents include following several aspects.(1) Medical image data density distribution.Nonparameter density functions and mixture density functions have been constructed to recognize medical images.The gray and its density of each pixel in image are the main expression content.Kernel density estimations and mixture density functions are deeply probed and heir corresponding models are constructed to estimate medical image data.(2) Medical image recognition based on density clustering.From the view of clustering analysis,algorithms of hill-climbing clustering based on kernel density model and weighted clustering based on Gaussian mixture density model for medical image data are presented.Both of the clustering algorithms are applied to our experiment.The results show that they can classify the semantic information of human medical images and have the effects of recognizing medical images.This method of medical image recognition offers new technique supports for medical image automatically segmentation.(3) Medical image recognition based on multi-feature fuse.Research works have been done around the topic of multi-feature fuse for medical images.Many features of medical images can express contents of medical images,so this paper try to recognize medical image from the image features fuse.On the basis of features expression for medical image content,data fuse on the level of image features is studied systematically.Framework of fusing medical image features data is designed. Methods and techniques based on multi-feature fuse are implemented.(4) Medical image association rule recognition based on fused features.The method of medical image association rule recognition based on fused feature is provided.All feature items of medical image are considered in this method.Frequent item sets in training samples are mined by our frequent close items set method and strong association rules between class labels are mined to construct classifiers which can make the conclusion that the medical image is normal or not.This method can help doctors to improve the accuracy of diagnosis.The many innovate research fruits,methods and algorithms for medical image recognition based on density clustering and multi-features fuse,and association rule recognition based on features fused have,offer new theory basis and operation methods for medical image recognition,automatically diagnosis and forepart clinic diagnosis.