A Rough Set Bounded Spatially Constrained Asymmetric Gaussian Mixture Model for Image Segmentation - Fig 10

<p>DC values for: (a) GM segmentation, (b) WM segmentation, (c) CSF segmentation, (d) CCR values over the entire images obtained by applying six segmentation algorithms to simulated brain MR images with increasing noise levels.</p

Illustration of three rough regions.

<p>Illustration of three rough regions.</p

Illustrations of proposed algorithm.

<p>Illustrations of proposed algorithm.</p

Computational complexity, converging time, number of iterations and per iteration time (average ± standard deviation, UNIT: Second) by applying five algorithms on BrainWeb dataset.

<p>Computational complexity, converging time, number of iterations and per iteration time (average ± standard deviation, UNIT: Second) by applying five algorithms on BrainWeb dataset.</p

Example of tissue surfaces for case IBSR12.

<p>(a) and (h) show ground truth of GM and WM surfaces, respectively. (b) to (g) show GM surface obtained by SCGM-EM, FRSCGMM, BAMM, BGGMM, GRFCM, and the proposed method, respectively. (i) to (n) show the WM surface obtained by SCGM-EM, FRSCGMM, BAMM, BGGMM, GRFCM, and the proposed method, respectively.</p

Examples of testing images.

<p>From left to right: synthetic image, simulated T1-weighted brain MR, real T1-weighted brain MR and natural images.</p

Restoration results on a Cameraman image degraded by a average kernel and a gaussian noise with

<p>. A. Original Image B. Degraded Image C. Operator Splitting TV D. ForWard E. FTVd F. FAST-TV G. NLTV+BOS H. Algorithm 1 I. Algorithm 2.</p

Illustrations of estimated distributions on natural image.

<p>Illustrations of estimated distributions on natural image.</p

PRI values of image segmentation results on Berkeley’s color image dataset.

<p>PRI values of image segmentation results on Berkeley’s color image dataset.</p

An example of spatial filters that considers four directions.

<p>From left to right: horizontal, vertical and two diagonal directions.</p