ITK. Ch 9 Segmentation Confidence Connected Isolated Connected Confidence Connected in Vector Images Jin-ju Yang

9.1.4 Confidence Connected Examples/Segmentation/ConfidenceConnected.cxx. First, the algorithm computes the mean and standard deviation of intensity values for all the pixels currently included in the region. A user-provided factor(ƒ) is used to multiply the standard deviation and define a range around the mean. Neighbor pixels whose intensity values fall inside the range are accepted and included in the region. When no more neighbor pixels are found that satisfy the criterion, the algorithm is considered to have finished its first iteration. At that point, the mean and standard deviation of the intensity levels are recomputed using all the pixels currently included in the region. This mean and standard deviation defines a new intensity range that is used to visit current region neighbors and evaluate whether their intensity falls inside the range. This iterative process is repeated until no more pixels are added or the maximum number of iterations is reached.

-> When faced with noisy images, it is usually convenient to pre- process the image by using an edge preserving smoothing filter. Argument => Input output seed(X, Y) -> When faced with noisy images, it is usually convenient to pre- process the image by using an edge preserving smoothing filter. -> In this case the float type is used for the pixels due to the requirements of the smoothing filter.

->Declare the type of the region growing filter -> The cast filter is required here to convert float pixel types to integer types since only a few image file formats support float types. homogeneous regions may only require a couple of iterations. Inhomogeneous fields may require more iterations. f defines how large the range of intensities will be. In practice, it seems to be more important to carefully select the multiplier factor than the number of iterations.

Input WM( 60,116)Ventricle( 81,112)GM( 107,69) Results ƒ = 2.5 (default value) This illustrates the vulnerability of the region growing methods when the anatomical structures to be segmented do not have a homogeneous statistical distribution over the image space.

ƒ = 1.5 ƒ = 3.5 WM( 60,116)Ventricle( 81,112)GM( 107,69) WM( 60,116)Ventricle( 81,112)GM( 107,69) Small values of the multiplier will restrict the inclusion of pixels to those having very similar intensities to those in the current region. Larger values of the multiplier will relax the accepting condition and will result in more generous growth of the region.

iteration=5, ƒ = 2.5 (default value) iteration = 1 iteration = 2iteration = 3iteration = 4 This iterative process is repeated until no more pixels are added or the maximum number of iterations is reached.

9.1.5 Isolated Connected Examples/Segmentation/IsolatedConnectedImageFilter.cxx. This filter is a close variant of the ConnectedThresholdImageFilter. In this filter two seeds and a lower threshold are provided by the user. The filter will grow a region connected to the first seed and not connected to the second one. In order to do this, the filter finds an intensity value that could be used as upper threshold for the first seed. A binary search is used to find the value that separates both seeds.

Argument => Input output seed1(X, Y) lower seed2(X, Y)

->by user GM seed -> by user WM seed ->by user

Results The selection of threshold values should therefore be performed in the smoothed image since the distribution of intensities could be quite different from that of the input image. This filter is intended to be used in cases where adjacent anatomical structures are difficult to separate. Selecting one seed in one structure and the other seed in the adjacent structure creates the appropriate setup for computing the threshold that will separate both structures. inputoutput Smoothed image

9.1.6 Confidence Connected in Vector Images Examples/Segmentation/VectorConfidenceConnected.cxx. This example illustrates the use of the confidence connected concept applied to images with vector pixel types. The basic difference between the scalar and vector version is that the vector version uses the covariance matrix instead of a variance, and a vector mean instead of a scalar mean. The membership of a vector pixel value to the region is measured using the Mahalanobis distance.