A Leukocyte image fast scanning based on max–min distance clustering

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 (100 x ) objective. Due to the larger¯eld of view of low power (10 x ) objective, the captured low power blood smear images can be used to locate leukocytes. All of the located positions make up a speci¯c routine, if we scan the blood smear along this routine with high power objective, there will be de¯nitely 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. The experimental results show that the proposed method can shorten scanning time from 8.0-14.0 min to 2.5-4.0 min while extracting 110 nonredundant individual high power leukocyte images.

A Novel Double Cluster and Principal Component Analysis-Based Optimization Method for the Orbit Design of Earth Observation Satellites

“…The candidate orbits could be clustered according to the principal components to obtain the optimal class [33,34]. The max-min distance method [35] is employed in this research. To avoid randomly selecting the first cluster center, the orbit most close to the zero point of principal components is selected as the first cluster center.…”