Single-cell image segmentation method

Abstract

The invention discloses a single-cell image segmentation method. The method includes the following steps that: 1) image preprocessing is performed: an image is converted into a grayscale image, noisesare removed, and contrast enhancement is performed; 2) block threshold segmentation is performed so as to segment the image into A*A small blocks, the optimal threshold of each block is calculated byusing the OSTU, so that the foreground and the background of each block can be separated from each other; 3) whether nucleus state features obtained by the previous segmentation step are normal or not is judged, if the nucleus state features are normal, it is proved that a segmentation result is relatively good, and the result is outputted; 4) if the segmentation result does not conform to the nucleus state features, the result is an inaccurate segmentation result, a next step of image processing is performed; and 5) adaptive threshold segmentation is performed, the result of the adaptive threshold segmentation is outputted together with other normal segmented images. With the single-cell image segmentation method of the present invention adopted, the problems of inaccurate nucleus segmentation and slow segmentation speed can be solved. The single-cell image segmentation method combines the advantage of high speed of the block segmentation threshold segmentation and the advantages ofhigh accuracy and low workload of the adaptive threshold segmentation; and since the advantage of the block segmentation threshold segmentation and the advantages of the adaptive threshold segmentation are complementary, the quality of an output image can be improved.

Description

technical field [0001] The invention relates to the technical field of cell image segmentation and recognition, in particular to a single cell image segmentation method. Background technique [0002] Utilizing the image processing technology and the clinical experience of pathology experts, we can quickly screen and count the images of cervical cancer cells, and then realize the automatic diagnosis of cervical cell images, which can greatly improve the efficiency of doctors and reduce the need for manual reading. Mistakes and misjudgments. At present, the automatic segmentation and classification of cervical cell images are mainly used. According to the type of cells, a variety of cell image segmentation algorithms are used. Among them, threshold segmentation is the most widely used and simplest type of segmentation in image segmentation. method. [0003] However, due to the subjective factors of film production, most of the cervical cell images are relatively complex. They...

AI Technical Summary

Problems solved by technology

[0003] However, due to the subjective factors of film production, most of the cervical cell images are relatively complex. They are not as simple as the ideal cell image and have no impurities. Each cell exists independently and is easy to identify, which makes the subsequent image segmentation and Cell sorting poses many difficulties

The main difficulties are: 1) Overlap between cells; 2) Fuzzy borders of cells; 3) Impurities in cell images; 4) Inconsistent size, shape and texture of cell nuclei

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