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Proceedings Paper

Using trainable segmentation and watershed transform for identifying unilocular and multilocular cysts from ultrasound images of ovarian tumour
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Paper Abstract

Ovarian masses are categorised into different types of malignant and benign. In order to optimize patient treatment, it is necessary to carry out pre-operational characterisation of the suspect ovarian mass to determine its category. Ultrasound imaging has been widely used in differentiating malignant from benign cases due to its safe and non-intrusive nature, and can be used for determining the number of cysts in the ovary. Presently, the gynaecologist is tasked with manually counting the number of cysts shown on the ultrasound image. This paper proposes, a new approach that automatically segments the ovarian masses and cysts from a static B-mode image. Initially, the method uses a trainable segmentation procedure and a trained neural network classifier to accurately identify the position of the masses and cysts. After that, the borders of the masses can be appraised using watershed transform. The effectiveness of the proposed method has been tested by comparing the number of cysts identified by the method against the manual examination by a gynaecologist. A total of 65 ultrasound images were used for the comparison, and the results showed that the proposed solution is a viable alternative to the manual counting method for accurately determining the number of cysts in a US ovarian image.

Paper Details

Date Published: 10 May 2017
PDF: 8 pages
Proc. SPIE 10221, Mobile Multimedia/Image Processing, Security, and Applications 2017, 102210B (10 May 2017); doi: 10.1117/12.2267468
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)
Sabah Jassim, The Univ. of Buckingham (United Kingdom)

Published in SPIE Proceedings Vol. 10221:
Mobile Multimedia/Image Processing, Security, and Applications 2017
Sos S. Agaian; Sabah A. Jassim, Editor(s)

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