Leukocyte positioning and iterative segmentation method

Abstract

The invention belongs to the field of medical image processing, and particularly relates to a leukocyte positioning and iterative segmentation method. According to boundary density information and color information of a leukocyte picture, boundary density characteristic factors of each sliding window and scores of color characteristic factors are calculated. Partial windows with relatively high scores are selected. Afterwards, the positioning windows are processed for being integrated to a final leukocyte positioning window. The area of the leukocyte positioning window in a leukocyte picture is intercepted as a positioning subgraph. Leukocyte segmentation is performed based on positioning. A GrabCut algorithm is initialized by the positioning subgraph. Accurate leukocyte segmentation is gradually realized through an expansion iteration method. Furthermore pixel replacing preprocessing is performed on the initial graph of the GrabCut algorithm so that segmentation accuracy of the GrabCut can be improved to a certain extent. The leukocyte positioning and iterative segmentation method can effectively prevent influences of other factors such as erythrocyte in direct segmentation of the leukocyte on the original graph and furthermore can generate an accurate and effective segmentation effect.

Description

technical field [0001] The invention belongs to the field of medical image processing, in particular to a white blood cell location and iterative segmentation method. Background technique [0002] Image segmentation of white blood cells is an important step in the automatic identification of white blood cells, the basis of cell classification and counting, and even affects the later medical diagnosis. Blood cell images are complex in shape and size, and their staining is often uneven, even overlapping, coupled with the instability of the microscopic system and different lighting conditions, etc. These factors make cell segmentation a rich research field in the field of medical image processing. Challenging work. So far, although many scholars have discussed and proposed a variety of segmentation methods, however, there is no efficient and fast algorithm that can be well applied to the segmentation of white blood cell images. [0003] At present, there are several major cat...

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Problems solved by technology

However, this method needs to be learned, so the amount of calculation is large, and the different staining of white blood cells also leads to poor generalization of the detection effect and other adverse effects

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