Tumor Analysis of Dynamic Breast Elastography

• Main Authors: Guan-Ying Huang, 黃冠穎
• Other Authors: Ruey-Feng Chang
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/99366419619066710694

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 97 === Recently, the sonoelastography has been the most general technique to measure the tumor strain. In the sonoelastography, the physicians need to lightly compress a tumor to obtain a dynamic elastographic image sequence which is composed of continuous elastographic slice. A representative slice of the dynamic elastographic image sequence will be selected by the physician and the quality of this selected slice will affect the diagnosis result. The purpose of this study is to quantify the elastographic images quality and select a representative slice from an elastography movie file. This study also proposes a semi-automatic segmentation to find the tumor contour for calculating the hard ratio of tumor. Utilizing a group of seeds given by the user in the first slice, the automatic segmentation using the edge-detection and region growing methods is applied in the first slice and then the subsequent slices. Moreover, the seeds of the subsequent slices will be moved according to the tumor displacement to improve the segmentation results. After finding the tumor contours, two quality quantification methods, the signal to noise ratio of (SNRe) and contrast to noise ratio (CNRe) of elastographic slice, are computed according to the uniformity inside the selected region or the contrast of the tumor and the surrounding normal tissue. Finally, find a representative slice based on the quantification and use the selected slice to differentiate the benign and the malignant lesions. In this study, 141 biopsy-proved sonoelastography composed of 93 benign and 48 malignant masses are used to evaluate the performance of the quantification methods. In the experiments, the diagnosis results of the slices selected by two proposed methods are compared with those of the maximum compression slices, maximum strain slices, and the slices selected by physicians. The Mann-Whitney U test, performance indexes, and receiver operation curve (ROC) are applied to examine the effectiveness of the proposed quantification methods. According to the result of experiment, the accuracy, sensitivity, specificity, and the Az value for the SNRe are 84.40%, 83.33%, 84.95% and 0.90, respectively and for the CNRe are 82.27%, 79.17%, 83.87% and 0.88, respectively. We can conclude that using the quantification methods to select the representative slice of the elastography is practicable and more objective than that selected by the physician. Moreover, to reduce the run time of the quantification analysis in this paper, a smart fast-selection method is also proposed and only the tumor contour of the selected slice is required to be segmented. The fast-selection method can achieve an acceptable performance and greatly reduce the execution time of the analysis.