Cell classifying method based on EMD feature extraction and sparse representation
A feature extraction and sparse representation technology, applied in the field of medical hyperspectral classification and recognition, can solve the problems of low accuracy and time-consuming
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[0057] The basic flow of the method for cell classification based on EMD feature extraction and sparse representation of the present invention is as follows: figure 1 As shown, it specifically includes the following steps:
[0058] 1) First normalize the blood cell data, and then store the data and corresponding labels.
[0059] 2) Due to the large number of spectral bands of blood cells and the spatial correlation between each band, if all the bands are used, redundant information will be generated, which will increase the computational time overhead. In order to reduce the data volume of EMD feature extraction and improve the operation time, band selection is performed on the blood cell data first. The size of blood cell data selected in the experiment is 462×451×33. So choose 5 bands out of 33 bands, which are the 25th, 33rd, 20th, 30th and 32nd bands. The selected bands have the advantages of high information content, low correlation, large spectral difference, and good...
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