Small focal areas of acoustic shadowing in the breast

Mesurolle, Benoı̂t; Ariche-Cohen, M; Mignon, F; Guinebretière, Jean‐Marc; Tardivon, A.; Goumot, Pierre-Alain

doi:10.1002/jcu.10029

Cited by 1 publication

(1 citation statement)

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“…For breast US, an additional source of difficulty is the presence of posterior shadowing artifacts, a major source of which is the US attenuation due to the fibrous stroma caused by the tumor. 29 Previous research on the segmentation of breast masses on US images includes work by Horsch et al, 30 Xiao et al, 31 and Madabhushi et al 32 Their segmentation methods were applied to 2-D US images. In our study, we compared the classification accuracy when 2-D and 3-D active contour models were used for segmentation.…”

Section: Scalementioning

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: Scalementioning

confidence: 99%