&lt;title&gt;Computerized characterization of breast masses using three-dimensional ultrasound images&lt;/title&gt;

Sahiner, Berkman; LeCarpentier, Gerald L.; Chan, Heang Ping; Roubidoux, Marilyn A.; Petrick, Nicholas; Goodsitt, Mitchell M.; Sanjay-Gopal, S.; Carson, Paul L.

doi:10.1117/12.310905

Cited by 14 publications

(7 citation statements)

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“…A number of researchers have recently investigated the application of CAD to breast US images. [11][12][13][14] Chen et al 12 extracted autocorrelation features from rectangular regions of interest ͑ROIs͒ containing solid breast masses. Using a neural network classifier, they obtained an area A z under the receiver operating characteristic ͑ROC͒ curve of 0.956 for classification of a dataset of 140 biopsy-proven masses as malignant or benign.…”

Section: Introductionmentioning

confidence: 99%

Computerized characterization of breast masses on three‐dimensional ultrasound volumes

et al. 2004

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We are developing computer vision techniques for the characterization of breast masses as malignant or benign on radiologic examinations. In this study, we investigated the computerized characterization of breast masses on three-dimensional (3-D) ultrasound (US) volumetric images. We developed 2-D and 3-D active contour models for automated segmentation of the mass volumes. The effect of the initialization method of the active contour on the robustness of the iterative segmentation method was studied by varying the contour used for its initialization. For a given segmentation, texture and morphological features were automatically extracted from the segmented masses and their margins. Stepwise discriminant analysis with the leave-one-out method was used to select effective features for the classification task and to combine these features into a malignancy score. The classification accuracy was evaluated using the area Az under the receiver operating characteristic (ROC) curve, as well as the partial area index Az(0.9), defined as the relative area under the ROC curve above a sensitivity threshold of 0.9. For the purpose of comparison with the computer classifier, four experienced breast radiologists provided malignancy ratings for the 3-D US masses. Our dataset consisted of 3-D US volumes of 102 biopsied masses (46 benign, 56 malignant). The classifiers based on 2-D and 3-D segmentation methods achieved test Az values of 0.87+/-0.03 and 0.92+/-0.03, respectively. The difference in the Az values of the two computer classifiers did not achieve statistical significance. The Az values of the four radiologists ranged between 0.84 and 0.92. The difference between the computer's Az value and that of any of the four radiologists did not achieve statistical significance either. However, the computer's Az(0.9) value was significantly higher than that of three of the four radiologists. Our results indicate that an automated and effective computer classifier can be designed for differentiating malignant and benign breast masses on 3-D US volumes. The accuracy of the classifier designed in this study was similar to that of experienced breast radiologists.

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Section: Introductionmentioning

confidence: 99%

Computerized characterization of breast masses on three‐dimensional ultrasound volumes

et al. 2004

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“…3 Other researchers have concentrated on computer-extracted texture features 4,5 or rf signal characteristics. 6 Sahiner et al 7 have explored computerized characterization of breast masses using texture feature extracted from three-dimensional ultrasound images.…”

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confidence: 99%

Computerized diagnosis of breast lesions on ultrasound

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Giger

Venta³

et al. 2002

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We present a computer-aided diagnosis (CAD) method for breast lesions on ultrasound that is based on the automatic segmentation of lesions and the automatic extraction of four features related to the lesion shape, margin, texture, and posterior acoustic behavior. Using a database of 400 cases (94 malignant lesions, 124 complex cysts, and 182 benign solid lesions), we investigate the marginal benefit of each feature in our CAD method and the performance of our CAD method in distinguishing malignant lesions from various classes of benign lesions. Finally, independent validation is performed on our CAD method. Eleven independent trials yielded an average Az value of 0.87 in the task of distinguishing malignant from benign lesions.

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“…17,18 In our previous work using a limited data set, we investigated the use of texture features extracted from 3-D ultrasound images for characterization of breast masses as malignant or benign, 11 and the segmentation of these lesions using a 2-D active contour model. 13 In this study, we investigated the use of a 3-D active contour model for improved segmentation, and compared the computer characterization results based on segmentation using the 2-D and 3-D models on a larger data set.…”

Section: Introductionmentioning

confidence: 99%

“…The increasing use of sonography to further evaluate and characterize both cystic and solid breast masses has prompted a number of researchers to investigate the application of CAD to breast ultrasound images to improve the characterization accuracy. [11][12][13][14][15] 3-D ultrasonography is rapidly gaining popularity as it moves out of the research environment and into the clinical setting. 16 Current technology allows radiologists to obtain 3-D or volumetric sonograms during clinical examination.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional active contour model for characterization of solid breast masses on three-dimensional ultrasound images

et al. 2003

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The accuracy of discrimination between malignant and benign solid breast masses on ultrasound images may be improved by using computer-aided diagnosis and 3-D information. The purpose of this study was to develop automated 3-D segmentation and classification methods for 3-D ultrasound images, and to compare the classification accuracy based on 2-D and 3-D segmentation techniques. The 3-D volumes were recorded by translating the transducer across the lesion in the z-direction while conventional 2-D images were acquired in the x-y plane. 2-D and 3-D segmentation methods based on active contour models were developed to delineate the mass boundaries. Features were automatically extracted based on the segmented mass shapes, and were merged into a malignancy score using a linear classifier. 3-D volumes containing biopsy-proven solid breast masses were collected from 102 patients (44 benign and 58 malignant). A leave-one-out method was used for feature selection and classifier design. The area A z under the test receiver operating characteristic curves for the classifiers using the 3-D and 2-D active contour boundaries were 0.88 and 0.84, respectively. More than 45% of the benign masses could be correctly identified using the 3-D features without missing a malignancy. Our results indicate that an accurate computer classifier can be designed for differentiation of malignant and benign solid breast masses on 3-D sonograms.

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<title>Computerized characterization of breast masses using three-dimensional ultrasound images</title>

Cited by 14 publications

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Computerized characterization of breast masses on three‐dimensional ultrasound volumes

Computerized characterization of breast masses on three‐dimensional ultrasound volumes

Computerized diagnosis of breast lesions on ultrasound

Three-dimensional active contour model for characterization of solid breast masses on three-dimensional ultrasound images

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