Most region-based methods are simple, easy to implement, and execute fairly quickly. They tend to perform well in tasks such as area and volume calculations, and classification of easily distinguishable tissues. However, they do have certain drawbacks that can limit their usefulness. First, they provide no structural information about the regions they classify, because it is generally difficult to extract geometric information from a collections of pixels. Second, they can be confused by image anomalies such as false bridges between close, but separate regions, or by occlusions such as one region blocking the view of another region (see Figure ). More sophisticated region-based methods include Fourier pattern analysis  and neural networks models . In Fourier analysis, regions are decomposed into a regular grid pattern and an n-dimensional Fourier transform is applied to each subregion. The frequency and intensity distribution of transformed data can be compared with the expected distribution from training samples. Neural networks are similar to statistical image processing methods with the exception of the presence of hidden nodes that can regroup and transform the input data patterns. Although neural networks themselves suffer from many drawbacks, such as sensitivity to geometric scale, orientation, and intensity distribution, they have nonetheless been used with varying degrees of success in thousands of applications.
Figure: Illustration of two potential limitations of region-based classification methods. (a) Due to the limited spatial resolution of CT and MR images, bridging errors can occur between distinct anatomical structures. (b) Occlusion errors occur when one region overlaps another region and partially blocks the perceived boundary of one or both of these regions.