Cell classifying method based on EMD feature extraction and sparse representation

Abstract

The invention discloses a cell classifying method based on EMD feature extraction and sparse representation, and relates to an EMD-based cell feature extraction method. The method comprises steps: an orthogonal subspace projection (OSP) method is firstly used for carrying out band section on a medical hyperspectral image, dimensions are reduced, and data redundancy is reduced; and then, a two-dimensional EMD method is used for carrying out feature extraction on the data after dimension reduction, and the data are decomposed into a series of IMF components whose frequencies are arranged from high to low. A sparse representation classifier (SRC) is used for classifying the data, samples are classified through comparing residual, and when the residual obtained through calculation is small, the sample is divided into the class. In the cell classifying method based on EMD feature extraction and sparse representation, the EMD shows good time frequency characteristics, and an obvious potential and advantages exist in hyperspectral data feature extraction. Meanwhile, as the sparse representation classifier (SRC) is used, the classifying precision is more greatly ensured.

Description

technical field [0001] The invention relates to an EMD (empirical mode decomposition)-based cell feature extraction method, which is classified and identified by a sparse representation classification method, and belongs to the field of medical hyperspectral classification and identification. Background technique [0002] The traditional medical detection method is a series of chemical analysis methods, staining the tissue sections, the experimental process is complicated, the cycle is long, the speed is slow, the intensity is large, the error is large, and the repeatability of the measurement is poor. The identification of cancer cells is realized through human eye observation. Affected by the subjectivity of the experimenter, it is easy to cause misdiagnosis. With the development of imaging technology, medical diagnosis is increasingly dependent on imaging technology. Imaging modalities include magnetic resonance imaging (MRI), computed tomography (CT), sonography, nuclea...

AI Technical Summary

Problems solved by technology

In traditional medical treatment, doctors use the naked eye to observe the shape of lesion areas on medical images, and many medical images are generated every day, which is time-consuming and has low accuracy.

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