摘要：The segmentation of leukocytes and their components acts
as the foundation for all automated image-based hematological disease
recognition systems. Perfection in image segmentation is a necessary
condition for improving the diagnostic accuracy in automated cytology. Since
the diagnostic information content of the segmented images is plentiful,
suitable segmentation routines need to be developed for better disease
recognition. Clustering is an essential image segmentation procedure which
segments an image into desired regions. A judicious integration of rough
sets and fuzzy sets is suitably employed towards leukocyte segmentation
in a clustering framework. In this study, the goodness of fuzzy sets
and rough sets is suitably integrated to achieve improved segmentation
performance. The membership concept of fuzzy sets endow is efficient handling
of overlapping partitions, and the rough sets provide a reasonable solution to
deal with uncertainty, vagueness, and incompleteness in data. Such synergistic
combination gives the proposed scheme an edge over standard cluster-based
segmentation techniques, that is, K-means, K-medoid, fuzzy c-means, and rough
c-means. Comparative analysis reveals that the hybrid rough fuzzy c-means
algorithm is robust in segmenting stained blood microscopic images. The
accomplished segmented nucleus and cytoplasm of a leukocyte can be used
for feature extraction which leads to automated leukemia detection.
