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Our approach to segmentation is to take really big numbers and "de-average" them.
Rajapakse, J. C., Giedd, J. N. & Rapoport, J. L. Statistical approach to segmentation of single-channel cerebral MR images.
In this study, we propose a novel approach to segmentation, the Real-time Unsupervised Segmentation (RUS) method, which comprises three phases.
A different motion-based approach to segmentation is presented in [23].
Similar(56)
Review and comparison of various approaches to segmentation of the land surface reveals common features, and permits development of a broad theoretical basis for segmentation and for characterization of segments and their boundaries.
A number of approaches to segmentation are based on finding compact clusters in some feature space [30, 31].
There are many approaches to segmentation that involve varying amounts of operator input, each with their own advantages and disadvantages.
In the past decades, a large number of different approaches to segmentation have been put forward continuously [1, 2]. the active contour model that was firstly presented by Kass et al. [3] is one of the most famous and successful models for extracting objects in image segmentation.
Our approach to parasite segmentation builds on prior research on segmentation of cells and parasites in biological images [ 34- 36].
Volumes were measured by k-nearest neighbor based probabilistic segmentation, an automatic and validated approach to brain segmentation (15).
A dual modeling approach to automatic segmentation of cerebral T2 hyperintensities and T1 black holes in multiple sclerosis.
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