Table 1:

Advantages and disadvantages of semi-automatic segmentation methods commonly used in medical applications.

Method	Ref.	Advantages	Disadvantages
Thresholding	34	Simplest and faster segmentation approach. Useful to discriminate foreground from the background.	Accurate results are not obtained when there is no significant gray scale difference within the image. Sensitive to noise.
Snakes	35, 36	Simple to understand and easy to implement. Works well for images with good contrast between regions.	Less robust to noise than other methods. Not suitable for images whose limits are very smooth.
Level Set	37	Intrinsic, versatile and parameter-free. Works well on images with topological changes and curvature dependence.	Not works properly with complex topology images (poor topological adaptation). Requires heuristic splitting mechanisms and control point regridding mechanisms.
Fuzzy Connectedness	38-40	Easy to implement based on mathematical concepts. Fast, robust and works well in 3D segmentation. Requires only a seed to work. Nonlinear functions of arbitrary complexity can also be modeled.	Determination of fuzzy membership is not easy. Automatic calculation of the membership (dynamic weights) could cause excessive computational cost, time and/or memory.
Clustering	41	Eliminates noisy spots. Reduces false blobs. Allows definition of more homogeneous regions.	Computationally expensive. Senstive to normalization or standardization processes. Very sensitive to outliers.
Region Growing	42	Simple concept. Requires only a few seed points to work. Able to identify the connected regions with the same characteristics. Provides good limit information of the image as well.	Computational cost is considerable. Over-division and voids are caused when the image shows gray scale irregularity and excessive noise.