Leukemia is an abnormal proliferation of white blood cells in bone marrow and blood. The abnormal increase of immature white blood cell count and the decrease of other blood cell counts may be signs of leukemia. Usually, pathologists diagnose leukemia by observing blood smears under a microscope. Bone marrow examination can be recommended to confirm and identify the specific type of leukemia. These routine methods are time-consuming and may be affected by the skills and expertise of the medical staff involved in the diagnostic process. Image processing based methods can be used to analyze microscopic smear images to automatically and quickly detect the incidence rate of leukemia. Image segmentation is one of the important tasks in processing and analyzing medical images. In this paper, we try to review the relevant works in the medical image processing field of blood smear images, focus on the automatic detection of leukemia, and classify and discuss the works in the relevant fields according to the segmentation methods used.
The pantograph arc is an important factor affecting the safety of urban rail transit trains. Based on the monitoring video of Zhengzhou subway train pantograph, this paper proposes a pantograph arc detection method using correlation filtering algorithm. A double-filter structure is adopted, and two filters are trained according to different characteristics of the pantograph-catenary arc, and the tracking and arc detection tasks are completed through the oscillation of the filters. Experiments were carried out on Zhengzhou subway surveillance video, the result shows the method achieves the expected correct rate, has low implementation cost and fast detection speed, and can be simply transplanted into practical engineering detection applications.
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