Cell image segmentation method based on automatic feature learning

Abstract

The invention relates to a cell image segmentation method based on automatic feature learning. As a method for learning features of cell images is very good in feature learning capacity, the cell segmentation accuracy can be greatly improved, and meanwhile, a random forest classifier does not need to select the features, so that the method is capable of well solving the confronted problems of feature extraction and selection in a recognition process. The cell image segmentation method based on the automatic feature learning comprises the following steps: 1, preprocessing: preprocessing initial cell images in a training set and a test set; (2) training a feature extractor; (3) performing recognition by utilizing the random forest classifier; and (4) postprocessing.

Description

technical field [0001] The invention relates to the field of biomedical image segmentation, in particular to a cell image segmentation method based on automatic feature learning. Background technique [0002] In order to better study the learning mechanism of the brain, brain scientists need to reconstruct brain neurons in three-dimensional space, and the basis and key of three-dimensional reconstruction is the segmentation of two-dimensional nerve cell images. Therefore, the accuracy of nerve cell segmentation directly affects the effect of 3D reconstruction. At present, based on machine learning, especially supervised learning, it has become a trend to realize automatic, accurate, fast and adaptive segmentation methods of nerve cell images. In view of the complex structure of cell images and the existence of various noises, this means that the segmentation of cell images requires more detailed features with significant distinguishing power. Therefore, under the premise th...

AI Technical Summary

Problems solved by technology

In addition, in the traditional classification model, the feature extraction is a manually designed feature, and it is not portable, while the classifier is universal, which limits the application of traditional methods.

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