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The physiopathology state of human breast tissue can be reflected by the electrical impedance spectral characteristics. In this paper, breast tissue classification based on the electrical impedance spectral characteristics was proposed for the breast disease diagnosis in the early stage. 9 breast tissue characteristics were obtained by measuring the electrical impedance spectra of the breast tissue which is collected in vitro and fresh, and then the breast tissue can be classified by the support vector machine. The experimental results show that this method can classify the breast tissue effectively with accuracy in more than 80%. Particularly, the classification accuracy of breast cancer and fatty tissue are close to 100%.

Histogram Intersection Kernel Support Vector Machines (SVM) was used for the image classification problem. Specifically, each image was split into blocks, and each block was represented by the Scale Invariant Feature Transform (SIFT) descriptors; secondly, k-means cluster method was applied to separate the SIFT descriptors into groups, each group represented a visual keywords; thirdly, count the number of the SIFT descriptors in each image, and histogram of each image should be constructed; finally, Histogram Intersection Kernel should be built based on these histograms. In our experimental study, we use Corel-low images to test our method. Compared with typical RBF kernel SVM, the Histogram Intersection kernel SVM performs better than RBF kernel SVM.

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