Slow characteristic based cell division recognition method and recognition device thereof

Abstract

The invention discloses a slow characteristic based cell division recognition method and a recognition device thereof. The method comprises the steps of extracting cell data by adopting a mode of unsupervised slow characteristic analysis so as to acquire a slow characteristic function; solving an accumulative square offset characteristic of the cell slow characteristic, and acquiring arrangement of variation rates of the slow characteristic from small to large; carrying out detection on the final accumulative square offset characteristic by using a method of model learning, and acquiring the probability of containing mitosis in the variation process of the cell data along with the time, wherein the test data contains mitosis if an output category label is 1, and the test data does not contain mitosis if the output category label is 0. The device comprises a first acquisition module, a second acquisition module, a third acquisition module and an output module. According to the invention, the difficulty of cell characteristic extraction is reduced, and the accuracy of cell characteristic extraction is improved, thereby providing good conditions for subsequent recognition and classification for dividing cells, and being convenient for recognition tracking and processing of the cells.

Description

technical field [0001] The invention relates to the field of image feature and pattern recognition, in particular to a slow feature-based cell division recognition method and a recognition device thereof, and in particular to the application of the slow feature to the field of cells. Background technique [0002] Cell biology is an important subject that studies cell structure, function, and living organisms. Cells promote the development of organisms through processes such as growth, division, aging, and death. Among them, cell division promotes the growth and development of organisms, as well as metabolism, which is of great significance to the process of cell growth. Then, how to quickly and accurately identify the process of cell division is of inestimable value to the study of cell changes and the development of things. [0003] In the research process of cell data, due to the large amount of original image data, it is usually not used as a feature to directly partici...

AI Technical Summary

Problems solved by technology

[0006] 1) The existing shape or structure feature extraction methods require accurate segmentation of cell regions, but microscope images often contain strong noise, making it difficult to accurately segment cells, so methods based on shape features usually have poor performance and generalization ability lower;

[0007] 2) Cell-based color feature extraction, because cells are relatively small, difficult to observe, and are seriously affected by background, staining, etc., so observation and tracking are relatively difficult, and there are problems such as high misjudgment rate and low work efficiency;

[0008] 3) Recognition methods based on phase models often require large-scale timing information to construct complex models. At the same time, the learning of the model has a high computational complexity, which reduces the speed of cell division recognition.

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