An application of heuristic search methods to edge and contour detection
Communications of the ACM
Segmentation of Blood Images Using Morphological Operators
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Deformable Contour Method: A Constrained Optimization Approach
International Journal of Computer Vision
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Blood cell identification and segmentation by means of statistical models
ISCGAV'07 Proceedings of the 7th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision
New Resolution Independent Measures of Circularity
Journal of Mathematical Imaging and Vision
Detecting leukaemia (AML) blood cells using cellular automata and heuristic search
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
Abnormal gastric cell segmentation based on shape using morphological operations
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part II
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We present an unsupervised blood cell segmentation algorithm for images taken from peripheral blood smear slides. Unlike prior algorithms the method is fast; fully automated; finds all objects---cells, cell groups and cell fragments---that do not intersect the image border; identifies the points interior to each object; finds an accurate one pixel wide border for each object; separates objects that just touch; and has been shown to work with a wide selection of red blood cell morphologies. The full algorithm was tested on two sets of images. In the first set of 47 images, 97.3% of the 2962 image objects were correctly segmented. The second test set---51 images from a different source---contained 5417 objects for which the success rate was 99.0%. The time taken for processing a 2272x1704 image ranged from 4.86 to 11.02 seconds on a Pentium 4, 2.4 GHz machine, depending on the number of objects in the image.