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Statistical Region-Based Segmentation of Ultrasound Images by Greg Slabaugh; Gozde Unal; Micheal Wels; Tong Fang; Bimba Rao is a scholarly article available to read on EtoBox.

What is Statistical Region-Based Segmentation of Ultrasound Images about?

Segmentation of ultrasound images is a challenging problem due to speckle, which corrupts the image and can result in weak or missing image boundaries, poor signal to noise ratio and diminished contrast resolution. Speckle is a random interference pattern that is characterized by an asymmetric distribution as well as significant spatial correlation. These attributes of speckle are challenging to model in a segmentation approach, so many previous ultrasound segmentation methods simplify the problem by assuming that the speckle is white and/or Gaussian distributed. Unlike these methods, in this article we present an ultrasound-specific segmentation approach that addresses both the spatial correlation of the data as well as its intensity distribution. We first decorrelate the image and then apply a region-based active contour whose motion is derived from an appropriate parametric distribution for maximum likelihood image segmentation. We consider zero-mean complex Gaussian, Rayleigh, and Fisher-Tippett flows, which are designed to model fully formed speckle in the in-phase/quadrature (IQ), envelope detected, and display (log compressed) images, respectively. We present experimental re

Author

Greg Slabaugh; Gozde Unal; Micheal Wels; Tong Fang; Bimba Rao

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0301-5629)

Published

2009

Language

EN