Abstract
A leukocyte image fast scanning method based on max-min distance clustering is proposed. Because of the lower proportion and uneven distribution of leukocytes in human peripheral blood, there will not be any leukocyte in lager quantity of the captured images if we directly scan the blood smear along an ordinary zigzag scanning routine with high power (100x) objective. Due to the larger field of view of low power (10x) objective, the captured low power blood smear images can be used to locate leukocytes. All of the located positions make up a specific routine, if we scan the blood smear along this routine with high power objective, there will be definitely leukocytes in almost all of the captured images. Considering the number of captured images is still large and some leukocytes may be redundantly captured twice or more, a leukocyte clustering method based on max–min distance clustering is developed to reduce the total number of captured images as well as the number of redundantly captured leukocytes. This method can improve the scanning efficiency obviously. The experimental results show that the proposed method can shorten scanning time from 8.0–14.0[Formula: see text]min to 2.5–4.0[Formula: see text]min while extracting 110 nonredundant individual high power leukocyte images.
Highlights
Quantities of di®erent types of leukocytes[1] in human peripheral blood is the most commonly performed medical data to play a vital role in the diagnosis of various diseases, researching leukocyte image fast scanning is meaningful
Experimental results indicate that leukocyte image fast scanning method based on max– min distance clustering has a higher e±ciency
In order to verify the feasibility of the proposed method, lots of experiments using an improved OLYMPUS BX53 microscope which can realize leukocyte image auto-scanning have been carried out
Summary
A leukocyte image fast scanning method based on max-min distance clustering is proposed. All of the located positions make up a specic routine, if we scan the blood smear along this routine with high power objective, there will be denitely leukocytes in almost all of the captured images. Considering the number of captured images is still large and some leukocytes may be redundantly captured twice or more, a leukocyte clustering method based on max–min distance clustering is developed to reduce the total number of captured images as well as the number of redundantly captured leukocytes. This method can improve the scanning e±ciency obviously.
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