Edge Preserving Image Segmentation using Spatially
Constrained EM Algorithm
Meena Ramasamy1 and Shantha Ramapackiam2
1Department
of Electronics and Communication Engineering, Sethu Institute of Technology,
India
2Department of Electronics
and Communication Engineering, Mepco Schlenk Engineering College, India
Abstract: In this paper, a new method for edge preserving image segmentation
based on the Gaussian Mixture Model (GMM) is presented. The standard GMM
considers each pixel as independent and does not incorporate the spatial
relationship among the neighboring
pixels. Hence segmentation is highly sensitive to noise. Traditional smoothing
filters average the noise, but fail to preserve the edges. In the proposed
method, a bilateral filter which employs two filters - domain filter and range
filter, is applied to the image for edge preserving smoothing. Secondly, in the
Expectation Maximization algorithm used to estimate the parameters of GMM, the
posterior probability is weighted with the Gaussian kernel to incorporate the
spatial relationship among the neighboring pixels. Thirdly, as an outcome of the
proposed method, edge detection is also done on images with noise. Experimental
results obtained by applying the proposed method on synthetic images and
simulated brain images demonstrate the improved robustness and effectiveness of
the method.
Keywords: Gaussian mixture model, expectation maximization,
bilateral filter, image segmentation.