|Title:||Computer-Aided Diagnosis Based on Speckle Patterns in Ultrasound Images||Authors:||Moon, W.K.
|Issue Date:||2012||Journal Volume:||38||Journal Issue:||7||Start page/Pages:||1251-1261||Source:||Ultrasound in Medicine and Biology||Abstract:||
For breast ultrasound, the scatterer number density from backscattered echo was demonstrated in previous research to be a useful feature for tumor characterization. To take advantage of the scatterer number density in B-mode images, spatial compound imaging was obtained, and the statistical properties of speckle patterns were analyzed in this study for use in distinguishing between benign and malignant lesions. A total of 137 breast masses (95 benign cases and 42 malignant cases) were used in the proposed computer-aided diagnosis (CAD) system. For each mass, the average number of speckle pixels in a region of interest (ROI) was calculated to use the concept of scatterer number density. In addition, the first-order and second-order statistics of the speckle pixels were quantified to obtain the distributions of the pixel values and the spatial relations among the pixels. The performance of the speckle features extracted from each ROI was compared with the performance of the segmentation features extracted from each segmented tumor. As a result, the proposed CAD system using the speckle features achieved an accuracy of 89.1% (122/137); a sensitivity of 81.0% (34/42); and a specificity of 92.6% (88/95). All of the differences between the speckle features and the segmentation features are not statistically significant (p > 0.05). In a receiver operating characteristic (ROC) curve analysis, the Az value, area under ROC curve, of the speckle features was significantly better than the Az value of the segmentation features (0.93 vs. 0.86, p = 0.0359). The performance of this approach supports the notion that the speckle patterns induced by the scatterers in tissues can provide information for classifying tumors. The proposed speckle features, which were extracted readily from drawing an ROI without any preprocessing, also provide a more efficient classification approach than tumor segmentation. ? 2012 World Federation for Ultrasound in Medicine & Biology.
|DOI:||10.1016/j.ultrasmedbio.2012.02.029||metadata.dc.subject.other:||Area under roc curve (AUC); Average numbers; B-mode images; Backscattered echoes; Breast Cancer; Breast mass; Breast ultrasound; CAD system; Classification approach; Compound imaging; Computer assisted diagnosis; First-order; Malignant lesion; Number density; Pixel values; Receiver operating characteristic curve analysis; Region of interest; Second order statistics; Spatial relations; Speckle patterns; Statistical properties; Tumor characterization; Tumor segmentation; Ultrasound images; Computer aided diagnosis; Image segmentation; Medical imaging; Pixels; Scattering; Tumors; Ultrasonics; Speckle; adult; aged; article; B scan; benign tumor; breast carcinoma; breast tumor; computer assisted diagnosis; diagnostic accuracy; echography; human; image analysis; image display; invasive papillary carcinoma; invasive tubular carcinoma; major clinical study; malignant neoplastic disease; papillary carcinoma; priority journal; radiation scattering; radiological parameters; receiver operating characteristic; sensitivity and specificity; speckle pattern; statistical analysis; Algorithms; Breast Neoplasms; Female; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Ultrasonography, Mammary
|Appears in Collections:||醫學系|
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