Edge detection in medical ultrasound images using adjusted Canny edge detection algorithm

Ultrasound medical images are very important component of the diagnostics process. They are widely used since ultrasound is a non-invasive and non-ionizing diagnostics method. As a part of image analysis, edge detection is often used for further segmentation or more precise measurements of elements...

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Bibliographic Details
Published in2016 24th Telecommunications Forum (TELFOR) pp. 1 - 4
Main Authors Nikolic, Marina, Tuba, Eva, Tuba, Milan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2016
Subjects
Biomedical imaging
Canny edge detector
Detectors
Edge detection
Image edge detection
Image processing
Image segmentation
Segmentation
Smoothing methods
Speckle
Ultrasonic imaging
Ultrasound images
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Summary:Ultrasound medical images are very important component of the diagnostics process. They are widely used since ultrasound is a non-invasive and non-ionizing diagnostics method. As a part of image analysis, edge detection is often used for further segmentation or more precise measurements of elements in the picture. Edges represent high frequency components of an image. Unfortunately, ultrasound images are subject to degradations, especially speckle noise which is also a high frequency component. That poses a problem for edge detection algorithms since filters for noise removal also degrade edges. Canny operator is widely used as an excellent edge detector, however it also includes Gaussian smoothing element that may significantly soften edges. In this paper we propose a modified Canny algorithm where Gaussian smoothing is replaced by modified median filter that successfully removes speckle noise with little degradation of edges followed by weak weighted smoothing filter that in a controlled way removes other noise, again with insignificant damage to the edges. Our proposed algorithm was tested on standard benchmark image and compared to other approaches from literature where it proved to be successful in precisely determining edges of internal organs.
DOI:10.1109/TELFOR.2016.7818878