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Proceedings Paper

Automatic segmentation and measurements of gestational sac using static B-mode ultrasound images
Author(s): Dheyaa Ahmed Ibrahim; Hisham Al-Assam; Hongbo Du; Jessica Farren; Dhurgham Al-karawi; Tom Bourne; Sabah Jassim
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Paper Abstract

Ultrasound imagery has been widely used for medical diagnoses. Ultrasound scanning is safe and non-invasive, and hence used throughout pregnancy for monitoring growth. In the first trimester, an important measurement is that of the Gestation Sac (GS). The task of measuring the GS size from an ultrasound image is done manually by a Gynecologist. This paper presents a new approach to automatically segment a GS from a static B-mode image by exploiting its geometric features for early identification of miscarriage cases. To accurately locate the GS in the image, the proposed solution uses wavelet transform to suppress the speckle noise by eliminating the high-frequency sub-bands and prepare an enhanced image. This is followed by a segmentation step that isolates the GS through the several stages. First, the mean value is used as a threshold to binarise the image, followed by filtering unwanted objects based on their circularity, size and mean of greyscale. The mean value of each object is then used to further select candidate objects. A Region Growing technique is applied as a post-processing to finally identify the GS. We evaluated the effectiveness of the proposed solution by firstly comparing the automatic size measurements of the segmented GS against the manual measurements, and then integrating the proposed segmentation solution into a classification framework for identifying miscarriage cases and pregnancy of unknown viability (PUV). Both test results demonstrate that the proposed method is effective in segmentation the GS and classifying the outcomes with high level accuracy (sensitivity (miscarriage) of 100% and specificity (PUV) of 99.87%).

Paper Details

Date Published: 19 May 2016
PDF: 13 pages
Proc. SPIE 9869, Mobile Multimedia/Image Processing, Security, and Applications 2016, 98690B (19 May 2016); doi: 10.1117/12.2224514
Show Author Affiliations
Dheyaa Ahmed Ibrahim, The Univ. of Buckingham (United Kingdom)
Hisham Al-Assam, The Univ. of Buckingham (United Kingdom)
Hongbo Du, The Univ. of Buckingham (United Kingdom)
Jessica Farren, Imperial College (United Kingdom)
Dhurgham Al-karawi, The Univ. of Buckingham (United Kingdom)
Tom Bourne, Imperial College (United Kingdom)
Sabah Jassim, The Univ. of Buckingham (United Kingdom)


Published in SPIE Proceedings Vol. 9869:
Mobile Multimedia/Image Processing, Security, and Applications 2016
Sos S. Agaian; Sabah A. Jassim, Editor(s)

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