Paper Info
Title
Spectral aggregation based on iterative graph cut for sonographic breast image segmentation
Abstract
In this paper, an image segmentation framework is proposed by unifying the techniques of spectral clustering and graph-cutting to address the difficult problem of breast lesion demarcation in sonography. In order to alleviate the effect of speckle noise and posterior acoustic shadows, the ROI of a sonogram is mapped to a specific eigen-space as an eigenmap by a constrained spectral clustering scheme. The eigen-mapping is boosted with the incorporation of partial grouping setting and then provide a useful preliminary aggregation based on intensity affinity. Following that, an iterative graph cut framework is carried out to identify the object of interest in the projected eigenmap. The proposed segmentation algorithm is evaluated with four sets of manual delineations on 110 breast ultrasound images. The experiment results corroborates that the boundaries derived by the proposed algorithm are comparable to manual delineations and hence can potentially provide reliable morphological information of a breast lesion.
Year
Venue
Keywords
2010
MIAR
breast ultrasound image,spectral clustering,iterative graph cut framework,image segmentation framework,manual delineation,proposed segmentation algorithm,spectral aggregation,sonographic breast image segmentation,breast lesion,proposed algorithm,projected eigenmap,breast lesion demarcation,gaussian mixture model,graph cut,speckle noise,image segmentation
Field
DocType
Volume
Cut,Breast ultrasound,Computer vision,Spectral clustering,Scale-space segmentation,Pattern recognition,Segmentation,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Speckle noise,Mathematics
Conference
6326
ISSN
ISBN
Citations 
0302-9743
3-642-15698-3
0
PageRank 
References 
Authors
0.34
12
4
Name
Order
Citations
PageRank
Chi-Hsuan Tsou100.68
Jie-Zhi Cheng210213.00
Jyh-Horng Chen338242.09
Chung-Ming Chen417616.17