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首页> 外文期刊>Computational and mathematical methods in medicine >A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images
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A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images

机译：通过有效地结合超声图像纹理和形态特征的良性和恶性乳腺肿瘤分类方法

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The classification of benign and malignant based on ultrasound images is of great value because breast cancer is an enormous threat to women’s health worldwide. Although both texture and morphological features are crucial representations of ultrasound breast tumor images, their straightforward combination brings little effect for improving the classification of benign and malignant since high-dimensional texture features are too aggressive so that drown out the effect of low-dimensional morphological features. For that, an efficient texture and morphological feature combing method is proposed to improve the classification of benign and malignant. Firstly, both texture (i.e., local binary patterns (LBP), histogram of oriented gradients (HOG), and gray-level co-occurrence matrixes (GLCM)) and morphological (i.e., shape complexities) features of breast ultrasound images are extracted. Secondly, a support vector machine (SVM) classifier working on texture features is trained, and a naive Bayes (NB) classifier acting on morphological features is designed, in order to exert the discriminative power of texture features and morphological features, respectively. Thirdly, the classification scores of the two classifiers (i.e., SVM and NB) are weighted fused to obtain the final classification result. The low-dimensional nonparameterized NB classifier is effectively control the parameter complexity of the entire classification system combine with the high-dimensional parametric SVM classifier. Consequently, texture and morphological features are efficiently combined. Comprehensive experimental analyses are presented, and the proposed method obtains a 91.11% accuracy, a 94.34% sensitivity, and an 86.49% specificity, which outperforms many related benign and malignant breast tumor classification methods.
机译：基于超声图像的良性和恶性的分类具有很大的价值，因为乳腺癌对全世界对女性健康造成巨大威胁。尽管纹理和形态特征都是超声乳腺肿瘤图像的关键表示，但它们的直接组合对改善良性和恶性的分类来说，由于高维质地特征过于侵蚀，因此淹没了低维形态特征的影响。为此，提出了一种有效的质地和形态特征梳理方法，以改善良性和恶性的分类。首先，提取乳房超声图像的纹理（即局部二进制模式（LBP），取向梯度（HOG）的直方图和灰度的共发生矩阵（GLCM））和形态学（即形状复杂性）特征。其次，训练了用于纹理特征的支持向量机（SVM）分类器，并且设计了一种用于形态特征的幼稚贝叶斯（NB）分类器，以分别施加质地特征和形态特征的辨别力。第三，两个分类器（即，SVM和NB）的分类得分被加权融合以获得最终分类结果。低维非参数化的NB分类器有效地控制整个分类系统的参数复杂度与高维参数SVM分类器组合。因此，有效地结合了纹理和形态特征。提出了综合实验分析，提出的方法获得了91.11％的精度，灵敏度为94.34％和86.49％的特异性，这优于许多相关良性和恶性乳腺肿瘤分类方法。

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