Share Email Print
cover

Proceedings Paper

Automatic segmentation and classification of gestational sac based on mean sac diameter using medical ultrasound image
Author(s): Shan Khazendar; Jessica Farren; Hisham Al-Assam; Ahmed Sayasneh; Hongbo Du; Tom Bourne; Sabah A. Jassim
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Ultrasound is an effective multipurpose imaging modality that has been widely used for monitoring and diagnosing early pregnancy events. Technology developments coupled with wide public acceptance has made ultrasound an ideal tool for better understanding and diagnosing of early pregnancy. The first measurable signs of an early pregnancy are the geometric characteristics of the Gestational Sac (GS). Currently, the size of the GS is manually estimated from ultrasound images. The manual measurement involves multiple subjective decisions, in which dimensions are taken in three planes to establish what is known as Mean Sac Diameter (MSD). The manual measurement results in inter- and intra-observer variations, which may lead to difficulties in diagnosis. This paper proposes a fully automated diagnosis solution to accurately identify miscarriage cases in the first trimester of pregnancy based on automatic quantification of the MSD. Our study shows a strong positive correlation between the manual and the automatic MSD estimations. Our experimental results based on a dataset of 68 ultrasound images illustrate the effectiveness of the proposed scheme in identifying early miscarriage cases with classification accuracies comparable with those of domain experts using K nearest neighbor classifier on automatically estimated MSDs.

Paper Details

Date Published: 22 May 2014
PDF: 7 pages
Proc. SPIE 9120, Mobile Multimedia/Image Processing, Security, and Applications 2014, 91200A (22 May 2014); doi: 10.1117/12.2057720
Show Author Affiliations
Shan Khazendar, The Univ. of Buckingham (United Kingdom)
Jessica Farren, Imperial College Healthcare NHS Trust (United Kingdom)
Hisham Al-Assam, The Univ. of Buckingham (United Kingdom)
Ahmed Sayasneh, Imperial College Healthcare NHS Trust (United Kingdom)
Hongbo Du, The Univ. of Buckingham (United Kingdom)
Tom Bourne, Imperial College Healthcare NHS Trust (United Kingdom)
Sabah A. Jassim, The Univ. of Buckingham (United Kingdom)


Published in SPIE Proceedings Vol. 9120:
Mobile Multimedia/Image Processing, Security, and Applications 2014
Sos S. Agaian; Sabah A. Jassim; Eliza Yingzi Du, Editor(s)

© SPIE. Terms of Use
Back to Top