Application of feature-level data fusion in medical image retrieval
Yizheng Guo · Computer Engineering and Applications Journal · 2010
The results of medical image retrieval mainly depend on the quality of feature extraction.For the characteristics of the medical image,three typical feature extraction methods such as histograms,Gabor wavelet and invariant matrix are adopted to ex- tract color feature,texture feature and shape feature respectively.But those features extracted by the various methods are used to medical image retrieval directly,the results are not satisfactory.So a feature-level data fusion algorithm based on PCA is pro- posed,the influence of classification caused by the wide gap of the value in different features can be avoided,in the mean time, it can reduce the dimension and the redundancy of the features.The experiments proves that the fused features can express the content of the medical image better,and a better result can be gotten in the medical image retrieval.