Statistical Region Based Segmentation of Ultrasound Images

Ünal, Gözde and Slabaugh, Greg and Wels, Michael and Fang, Tong and Rao, Bimba (2008) Statistical Region Based Segmentation of Ultrasound Images. (Accepted/In Press)

Warning
There is a more recent version of this item available.
[thumbnail of UMB-D-08-00052.pdf] PDF
UMB-D-08-00052.pdf

Download (684kB)

Abstract

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 correla- tion. 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 paper 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 appropri- ate 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 results demon- strating the effectiveness of our method, and compare the results to other parametric and non-parametric active contours.
Item Type: Article
Uncontrolled Keywords: ultrasound imaging, segmentation, statistical segmentation
Subjects: Q Science > QA Mathematics > QA075 Electronic computers. Computer science
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Computer Science & Eng.
Faculty of Engineering and Natural Sciences > Academic programs > Electronics
Depositing User: Gözde Ünal
Date Deposited: 13 Nov 2008 15:40
Last Modified: 26 Apr 2022 08:25
URI: https://research.sabanciuniv.edu/id/eprint/10693

Available Versions of this Item

Actions (login required)

View Item
View Item