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\iteman{io-port 05489820}
\itemau{Lee, Gobert N.; Fukuoka, Daisuke; Ikedo, Yuji; Hara, Takeshi; Fujita, Hiroshi; Takada, Etsuo; Endo, Tokiko; Morita, Takako}
\itemti{Classification of benign and malignant masses in ultrasound breast image based on geometric and echo features.}
\itemso{Krupinski, Elizabeth A. (ed.), Digital mammography. 9th international workshop, IWDM 2008, Tucson, AZ, USA, July 20--23, 2008. Proceedings. Berlin: Springer (ISBN 978-3-540-70537-6/pbk). Lecture Notes in Computer Science 5116, 433-439 (2008).}
\itemab
Summary: The aim of this paper is to study the use of geometric and echo features in classifying masses in ultrasound images as benign or malignant. While mammography is very effective in detecting masses and other lesions, breast ultrasound is a valuable adjunct in distinguishing solid and fluid-filled masses where the former is mostly malignant and the latter benign. Six features including two geometric features and four echo features derived from the segmented mass and its neighboring regions are employed in this study. They are the compactness and orientation of the mass, two intensity ratios of the mass and its neighboring regions, homogeneity, and depth-to-width ratio of the mass. Linear discriminant analysis and receiver operating characteristic (ROC) analysis are employed for classification and performance evaluation. The area under the ROC curve (AUC) has a value of 0.940 using all breast masses for training and testing and 0.923 using the leave-one-mass-out cross-validation method. Clinically significance of the results will be evaluated using a larger dataset.
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\itemut{ultrasound breast mass; classification; geometric feature; echo feature}
\itemli{doi:10.1007/978-3-540-70538-3\_60}
\end