Method and system for recognizing pathological cells of cervical cell pathological section based on cell mass

Abstract

The invention discloses a cervical cell pathological slice image pathological cell identification method and system based on cell mass, which comprises the following steps: extracting a single cell mass region on the cervical pathological slice image; extracting the single cell mass region on the cervical pathological slice image; counting the size of the cell mass region, and treating the cell mass whose size exceeds a predetermined threshold value as a super-large cell mass; the superlarge cell mass being divided into daughter cell mass; the region to be recognized being obtained by boundaryfilling treatment of the cell cluster region other than the super-large cell cluster and the daughter cell cluster obtained by splitting; a lesion cell being identified in that region to be identified. The invention aims at the whole slice image of the massive pixels, takes the cell cluster as the processing and recognition unit, instead of the conventional image partition and fusion frame, whichis more suitable for the characteristics of the cervical cell pathological slice image, and improves the efficiency and the precision at the same time.

Description

technical field [0001] The invention belongs to the field of medical cytopathological image processing, and more specifically relates to a method and system for identifying lesion cells in cervical cytopathological slices based on cell clusters. Background technique [0002] Cervical cancer is a malignant tumor with high incidence in women. Cervical liquid-based cytopathology is currently the most important means of preventing and screening cervical cancer. Accurate interpretation of diseased cells in cytopathological slice images is an important basis for doctors to determine the patient's condition and formulate a treatment plan. At present, the manual interpretation of cytopathological images is not only time-consuming, but also the interpretation results are very dependent on the experience of doctors. Therefore, automatically interpreting diseased cells in pathological slides can not only improve the efficiency of diagnosis but also provide doctors with a more unified...

AI Technical Summary

Problems solved by technology

However, this block fusion scheme has the following disadvantages: the cells at the boundaries of the sub-blocks are artificially cut, reducing the recognition accuracy; too large a block is still difficult to deal with directly, and too small a block brings more boundary problems and increases processing overhead ;Inconsistency in processing results of adjacent blocks at subblock boundaries

Although the conventional image block fusion strategy can handle full slice images, it introduces redundant calculations and sub-block boundary problems at the same time.

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