Iterative Eigenmap Aggregation for Boundary Delineation on Ultrasound Images
Date Issued
2008
Date
2008
Author(s)
Tsou, Chi-Hsuan
Abstract
Breast sonography is one of the effective tools for identification of breast cancers. It has been widely used for diagnosis and screening because of its non-radiation, low cost and effectiveness. The lesion shape in a breast sonogram is a crucial indicator for differentiation of benign and malignant lesions. If a computer-aided diagnosis (CAD) system is able to delineate the lesion contour delineated accurately and proposes suggestive diagnoses, it would be of great help for medical doctors in distinguishing the benign breast lesions from malignant ones. Although the lesion shapes may be demarcated manually, it is usually to time-consuming to be practical. Therefore, it is compelling to develop semi-automatic or automatic segmentation algorithms to acquire lesion boundaries in practice.ltrasound image segmentation is a highly challenging task because of the high noise, low contrast, speckle, artifact, and surrounding tissue textures commonly found in a sonogram. These phenomena constitute a complicated image structure and result in inhomogeneous spatial distribution of gray levels. This study aims to delineate the boundary of the object of interest in an ultrasound image by referencing perceptual grouping in human vision and combining the image structure, prior knowledge and object morphology. The derived lesion boundaries can then be used as the basis of a CAD system, generating the features and suggestive diagnoses for the reference of medical doctors.his thesis proposes a new algorithm for boundary delineation of sonographic breast lesions, which integrates the image structure, prior knowledge and object morphology. The proposed algorithm is composed of two major steps. In the first step, the second eigen-vector map of the region of interest (ROI) is obtained by solving the constrained eigenvalue problem using eigen value decomposition. The energy function is constructed incorporating both boundary and regional properties of the ROI. In the second step, the lesion boundary is identified by an iterative graph cut approach. o evaluate the proposed algorithm, the lesion boundaries derived by using the proposed algorithm and those manually delineated boundaries by four expert observers on 110 breast sonograms have been compared, including 60 benign lesions and 50 malignant lesions. The results show that >75% of the derived boundaries lie within the span of the manually delineated boundaries. The Williams Index is 1.085 indicating that the derived boundaries agree as much with the manually delineated boundaries as the manually delineated boundaries agree with one another. The overlapping and difference ratios between the derived boundaries and the average manually delineated boundaries are mostly higher than 0.90 and lower than 0.14, respectively. The performance figures show that the propose algorithm is capable of deriving the lesion boundaries that are comparable to those demarcated manually, even for those breast sonograms with weak edges and artifacts.
Subjects
Ultrasound images
Image segmentation
Spectral clustering
Graph cuts
Tumor extraction
SDGs
Type
thesis
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