Automatic window width and window level extraction method based on neural network

Automatic window width and window level extraction method based on neural network

  • CN 103,310,227 A
  • Filed: 03/16/2012
  • Published: 09/18/2013
  • Est. Priority Date: 03/16/2012
  • Status: Active Application
First Claim
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1. the automatic window width and window level extracting method based on neural network is characterized in that, comprises the steps:

  • A) load the MR image;

    B) histogram feature and the spatial information feature of extraction MR image;

    C) according to histogram feature and the spatial information feature of MR image, utilize self-adaptation K clustering method that all MR images are classified;

    D) with the window width and window level information of such image feature information and image such radial basis function neural network is trained respectively;

    E) change when producing new MR image when the window width and window level that loads new MR image or certain class image, extract histogram feature and the spatial information feature of new MR image;

    F) according to histogram feature and the spatial information feature of new MR image, utilize described self-adaptation K clustering method that new MR image is classified;

    G) the new MR image of sorted every class is compared with existing every class image of having trained, calculate every class image and existing every class image similarity of having trained earlier, if it is dissimilar, then increase a new class at former basis of classification, if similar, judge again whether the window width and window level of such image is the same with the window width and window level goldstandard of existing training image, if it is the same, do not increase new class, if different, then increase a new class at former basis of classification;

    H) loading the MR image tests;

    I) extract histogram feature and the spatial information feature of testing the MR image;

    J) according to histogram feature and the spatial information feature of test MR image, calculate and the histogram feature of each cluster centre and the similarity of spatial information feature, obtain a plurality of cluster centres the most similar to it;

    K) utilize the radial base neural net of described a plurality of cluster centre correspondences to calculate a plurality of window width and window levels respectively;

    L) merge described a plurality of window width and window levels, export final window width and window position.

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