Feature Extraction from the Turning Angle Function for the Classification of Contours of Br.pdf

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2017-04-06 发布于江苏
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Feature Extraction from the Turning Angle Function for the Classification of Contours of Br.pdf

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Feature Extraction from the Turning Angle Function for the Classification of Contours of Br

Feature Extraction from the Turning Angle Function for the Classification of Contours of Breast Tumors Rangaraj M. Rangayyan, Denise Guliato ? , Juliano Daloia de Carvalho, and Se?rgio Anchieta Santiago Abstract— Malignant breast tumors and benign masses ap- pear in mammograms with different shape characteristics: the former usually have rough, spiculated, or microlobulated contours, whereas the latter commonly have smooth, round, oval, or macrolobulated contours. Features that characterize shape roughness and complexity can assist in distinguishing between malignant tumors and benign masses. Signatures of contours may be used to analyze their shapes. We propose to use the turning angle function of contours of breast masses to derive features that capture the characteristics of spicules and shape roughness as described above. We propose methods to derive an index of spiculation (SITA), index of convexity (CITA) and a measure of fractal dimension (FDTA) from the turning angle function. The methods were tested with a set of 111 contours of 65 benign masses and 46 malignant tumors. Classification accuracies of 0.92, 0.93, and 0.91, in terms of the area under the receiver operating characteristics curve, were obtained with SITA, CITA, and FDTA, respectively. I. ANALYSIS OF CONTOURS AND SIGNATURES A. Shape analysis of breast tumors Breast tumors and masses appear in mammograms with different shape characteristics: malignant tumors usually have rough, spiculated, or microlobulated contours, whereas benign masses commonly have smooth, round, oval, or macrolobulated contours [1], [2]. Measures that can quantita- tively represent shape roughness and complexity can assist in the classification of malignant tumors and benign masses [3], [4]. Objective features of shape complexity, such as compact- ness (C), fractional concavity (Fcc), spiculation index (SI), a Fourier-descriptor-based factor (FF ), fractal dimension (FD), moments, chord-length statistics, and wavelet trans- fo