Medical image classification method and system

Abstract

The invention provides a medical image classification method and system. The method comprises the steps of obtaining an image template, segmenting two areas of interest in the image template with tissue positions distributed symmetrically, obtaining the standard maps of the two areas of interest, registering the image template and the standard maps to each first image in a total image sample pool, segmenting the two areas of interest in each first image on the basis of the registered standard maps, calculating the first lateralization vectors of the two areas of interest in each first image, and training an image data classifier through the first lateralization vectors to obtain the trained image data classifier. The method can be applied to medical images except brain images for carrying out classification and treatment, is especially suitable for classification of brain medical images, the method is simple, and operation is easy.

Description

technical field [0001] The invention relates to medical image screening or classification technology, in particular to a medical image classification method and system. Background technique [0002] At present, the image screening or classification technology for medical images is mainly based on the screening and classification of brain images, and the screening and classification methods for brain images are usually based on MRI (magnetic resonance images) and PET (positive imaging). Electron emission computed tomography (CT) images, MRI and PET images provide neuropathological information from structural and functional aspects, and information fusion of MRI and PET can further improve computer-aided diagnosis. Classification of medical images based on MRI and PET images usually requires complex preprocessing steps. For example, in one of the preprocessing steps, the MRI and PET images are first preprocessed separately: the MRI image is segmented into gray matter, white m...

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