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This method needs trained man power and involves complicated calculations (Boman and Shukla 2009).
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Besides, the heuristic method needs to be trained which limits the generalization of the recognition method.
Journalists need training.
Doctors themselves need training.
In spite of background subtraction algorithms, the proposed method needs no primary training frame without foreground.
This method needs neither any training data nor any prior knowledge of SOI while suffering no loss of DOFs.
It means that LASSO-based LS method needs to have enough training set size to obtain the best effect.
Compared with [20, 21], the proposed method does not need training.
This method does not need training data and this advantage is crucially important when the training data is not available.
In addition, differing from some existing studies, this method does not need training samples and is relatively easy to implement in practice.
Classifiers are known as supervised methods because they need training data that are manually segmented by medical experts and then used as references for automatically segmenting new data [1, 2].
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