A simple and accurate method for white blood cells segmentation using K-means algorithm.
Omid SarrafzadehAlireza Mehri DehnaviHossein RabbaniArdeshir TalebiPublished in: SiPS (2015)
Keyphrases
- k means
- segmentation algorithm
- segmentation method
- high accuracy
- clustering method
- computationally efficient
- detection algorithm
- dynamic programming
- improved algorithm
- energy function
- preprocessing
- objective function
- optimization algorithm
- unsupervised clustering
- cost function
- computational cost
- final result
- classification algorithm
- completely automatic
- optimization method
- convergence rate
- detection method
- computational complexity
- test images
- clustering algorithm
- matching algorithm
- hierarchical clustering
- random walker
- learning algorithm
- probabilistic model
- pairwise
- optimal solution
- threshold selection
- prior information
- segmentation accuracy
- gray level images
- watershed algorithm
- fuzzy clustering algorithm
- region growing
- tree structure
- expectation maximization
- markov random field
- significant improvement
- input image
- image segmentation algorithms
- foreground background separation
- initial cluster centers
- image segmentation
- similarity measure
- spectral clustering
- edge detection
- recognition algorithm
- segmentation result
- thresholding method
- piecewise constant
- multiscale
- graph cuts
- optimal segmentation
- fully automatic
- object segmentation
- rough k means