Paper
14 December 2015 An effective segmentation method of ultrasonic thyroid nodules
Author Affiliations +
Proceedings Volume 9814, MIPPR 2015: Parallel Processing of Images and Optimization; and Medical Imaging Processing; 98140F (2015) https://doi.org/10.1117/12.2205406
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China
Abstract
Segmentation of ultrasound image is an important port of ultrasound medical computer-aided system. However, due to the speckle noise, intensity heterogeneity, and low contrast, the ultrasonic segmentation is much difficult. In this paper, we introduce an effective method which integrates edge phase information and an effective active contour model to make the segmentation better. First, we use the speckle reducing anisotropic diffusion method to suppress the noise in ultrasound images. Then, we utilize the local phase information from monogenic signal to form a novel edge indicator and we use the indicator to replace the traditional intensity-based speed stopping term in distance regularized level set evolution. Another contribution of this paper is that we extend the proposed method to the field of ultrasonic thyroid nodules segmentation, qualitative and quantitative comparative results demonstrate the outperformance of our approach.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Wenpeng Du and Nong Sang "An effective segmentation method of ultrasonic thyroid nodules", Proc. SPIE 9814, MIPPR 2015: Parallel Processing of Images and Optimization; and Medical Imaging Processing, 98140F (14 December 2015); https://doi.org/10.1117/12.2205406
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Ultrasonography

Image segmentation

Tissues

Ultrasonics

Speckle

Tumor growth modeling

Statistical modeling

Back to Top