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Automatic classification of breast tumors using circularly approximated contour

机译：使用圆形近似轮廓自动分类乳腺肿瘤

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In this paper a simple, novel and automatic method for classification of breast malignancy in digital mammogram images is proposed. It is based on the similarity/differences between the contour extracted from the mammogram image and its nearest circular approximation. The nearest circular approximation of extracted contour is obtained by considering the centroid of tumor as its centre and arithmetic mean of maximum and minimum radial distances of contour points from the centroid, as its radius. The similarity between the fitted circle and tumor contour is measured in terms of variance of radial distance of contour from the centroid, as a feature for classification. The simulation results show that for a set of 150 tumor contours, the proposed method gives 96.67% accuracy. The performance obtained in terms of the receiver operating characteristic (ROC) parameters like accuracy (Ac), sensitivity (Se), specificity (Sp), and positive (PPV) and negative predictive values (NPV) are 96.67%, 0.9873, 0.9437, 0.9512 and 0.9853 respectively.
机译：本文提出了一种简单，新颖的和自动化数字乳房图像图像乳腺恶性肿瘤分类的方法。它基于从乳房图图像提取的轮廓与其最近的圆形近似之间的轮廓之间的相似性/差异。通过将肿瘤的质心视为其中心和来自质心的轮廓点的最大和最小径向距离的中心和算术平均值，获得最近的提取轮廓的圆形近似。拟合圆和肿瘤轮廓之间的相似性在于从质心轮廓的径向距离的变化来测量，作为分类的特征。仿真结果表明，对于一组150个肿瘤轮廓，所提出的方法精度为96.67％。在接收器操作特征（ROC）参数等方面获得的性能，如精度（AC），灵敏度（SE），特异性（SP）和阳性（PPV）和负预测值（NPV）为96.67％，0.9873,0.9437， 0.9512和0.9853分别。

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