Cell image segmentation method based on automatic feature learning

A technology of feature learning and image segmentation, applied in the field of biomedical image segmentation, can solve the problems of limiting the application of traditional methods, lack of portability, etc., and achieve the effect of improving accuracy and robustness

Active Publication Date: 2013-10-23
山东幻科信息科技股份有限公司
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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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  • Cell image segmentation method based on automatic feature learning
  • Cell image segmentation method based on automatic feature learning
  • Cell image segmentation method based on automatic feature learning

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Embodiment Construction

[0033] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0034] Take the Drosophila nerve cell image as an example:

[0035] The data sets used in the present invention are two databases obtained from Drosophila nerve cell images observed with an electron microscope, and each database includes 30 original images. One of the databases is used as the training set and the other as the testing set. The training set provides raw images and corresponding expert hand-segmented results. (Example images of datasets like Figure 2a-Figure 2c )

[0036] The technical framework of the present invention is as figure 1 shown.

[0037] 1. Pretreatment

[0038] Because there are uneven gray levels and more noise in the original cell image, the original image should be preprocessed first. The preprocessing used in the present invention is to use histogram equalization and Gaussian filter technology to realize image enh...

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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...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/46
Inventor 尹义龙杨公平曹贵宝薛俊欣张彩明
Owner 山东幻科信息科技股份有限公司
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