Modeling Envelope Statistics of Blood and Myocardium for Segmentation of Echocardiographic Images

Maartje M. Nillesen*, Richard G.P. Lopata, Inge H. Gerrits, Livia Kapusta, Johan M. Thijssen, Chris L. de Korte

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The objective of this study was to investigate the use of speckle statistics as a preprocessing step for segmentation of the myocardium in echocardiographic images. Three-dimensional (3D) and biplane image sequences of the left ventricle of two healthy children and one dog (beagle) were acquired. Pixel-based speckle statistics of manually segmented blood and myocardial regions were investigated by fitting various probability density functions (pdf). The statistics of heart muscle and blood could both be optimally modeled by a K-pdf or Gamma-pdf (Kolmogorov-Smirnov goodness-of-fit test). Scale and shape parameters of both distributions could differentiate between blood and myocardium. Local estimation of these parameters was used to obtain parametric images, where window size was related to speckle size (5 × 2 speckles). Moment-based and maximum-likelihood estimators were used. Scale parameters were still able to differentiate blood from myocardium; however, smoothing of edges of anatomical structures occurred. Estimation of the shape parameter required a larger window size, leading to unacceptable blurring. Using these parameters as an input for segmentation resulted in unreliable segmentation. Adaptive mean squares filtering was then introduced using the moment-based scale parameter (σ2/μ) of the Gamma-pdf to automatically steer the two-dimensional (2D) local filtering process. This method adequately preserved sharpness of the edges. In conclusion, a trade-off between preservation of sharpness of edges and goodness-of-fit when estimating local shape and scale parameters is evident for parametric images. For this reason, adaptive filtering outperforms parametric imaging for the segmentation of echocardiographic images. (E-mail: m.m.nillesen@cukz.umcn.nl).

Original languageEnglish
Pages (from-to)674-680
Number of pages7
JournalUltrasound in Medicine and Biology
Volume34
Issue number4
DOIs
StatePublished - Apr 2008
Externally publishedYes

Funding

FundersFunder number
Philips Medical Systems
Stichting voor de Technische WetenschappenNKG 6466

    Keywords

    • Adaptive filtering
    • Echocardiography
    • Image segmentation
    • Speckle reduction
    • Speckle statistics
    • Ultrasound

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