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首页> 外文期刊>The Open Medical Informatics Journal >Automatic Detection and Classification of Breast Tumors in Ultrasonic Images Using Texture and Morphological Features
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Automatic Detection and Classification of Breast Tumors in Ultrasonic Images Using Texture and Morphological Features

机译：利用纹理和形态特征对超声图像中的乳腺肿瘤进行自动检测和分类

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摘要

Due to severe presence of speckle noise, poor image contrast and irregular lesion shape, it is challenging to build a fully automatic detection and classification system for breast ultrasonic images. In this paper, a novel and effective computer-aided method including generation of a region of interest (ROI), segmentation and classification of breast tumor is proposed without any manual intervention. By incorporating local features of texture and position, a ROI is firstly detected using a self-organizing map neural network. Then a modified Normalized Cut approach considering the weighted neighborhood gray values is proposed to partition the ROI into clusters and get the initial boundary. In addition, a regional-fitting active contour model is used to adjust the few inaccurate initial boundaries for the final segmentation. Finally, three textures and five morphologic features are extracted from each breast tumor; whereby a highly efficient Affinity Propagation clustering is used to fulfill the malignancy and benign classification for an existing database without any training process. The proposed system is validated by 132 cases (67 benignancies and 65 malignancies) with its performance compared to traditional methods such as level set segmentation, artificial neural network classifiers, and so forth. Experiment results show that the proposed system, which needs no training procedure or manual interference, performs best in detection and classification of ultrasonic breast tumors, while having the lowest computation complexity.
机译：由于斑点噪声的严重存在，图像对比度差和病变形状不规则，因此为乳房超声图像建立全自动检测和分类系统具有挑战性。本文提出了一种新颖有效的计算机辅助方法，该方法无需任何人工干预即可包括感兴趣区域（ROI）的生成，乳腺肿瘤的分割和分类。通过结合纹理和位置的局部特征，首先使用自组织映射神经网络检测ROI。然后，提出了一种考虑加权邻域灰度值的改进归一化裁剪方法，将ROI划分为多个簇并获得初始边界。此外，使用区域拟合活动轮廓模型来调整一些不准确的初始边界以进行最终分割。最后，从每个乳腺肿瘤中提取出三个纹理和五个形态特征。因此，无需任何培训过程，就可以使用高效的相似性传播聚类来满足现有数据库的恶性和良性分类。与传统方法（例如水平集分割，人工神经网络分类器等）相比，该系统在132个案例（67个良性和65个恶性肿瘤）中得到了验证。实验结果表明，该系统无需训练程序或人工干预，在超声乳腺肿瘤的检测和分类中表现最佳，同时计算复杂度最低。

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