Nonlocal means-based speckle filtering for ultrasound images
IEEE Transactions on Image Processing
Region-Based Active Contours with Exponential Family Observations
Journal of Mathematical Imaging and Vision
A comparative study of ultrasound image segmentation algorithms for segmenting kidney tumors
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
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Segmentation of ultrasound images is often a very challenging task due to speckle noise that contaminates the image. It is well known that speckle noise exhibits an asymmetric distribution as well as significant spatial correlation. Since these attributes can be difficult to model, many previous ultrasound segmentation methods oversimplify the problem by assuming that the noise is white and/or Gaussian, resulting in generic approaches that are actually more suitable to MR and X-ray segmentation than ultrasound. Unlike these methods, in this paper we present an ultrasound-specific segmentation approach that first decorrelates the image, and then performs segmentation on the whitened result using statistical region-based active contours. In particular, we design a gradient ascent flow that evolves the active contours to maximize a log likelihood functional based on the Fisher-Tippett distribution. We present experimental results that demonstrate the effectiveness of our method.