基于空间模糊c均值(SFCM)分类方法的甲状腺肿瘤诊断系统

Shankarlal B, P. Sathya
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引用次数: 0

摘要

该方法包括超声甲状腺图像的预处理、特征计算和诊断阶段。该方法的预处理阶段检测并降低源超声甲状腺图像中的噪声含量。然后,从预处理后的超声甲状腺图像中计算纹理特征;最后,采用空间模糊c均值分类(SFCM)方法将这些特征分为正常、轻度和重度。在DDTI和Open-CAS数据集的超声甲状腺图像上对该方法进行了准确率、精密度、召回率和诊断率的测试
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Thyroid Tumor Diagnosis System using Spatial Fuzzy C-Means (SFCM) Classification Approach
This methodology consists of preprocessing of ultra sound thyroid images, feature computations and diagnosis stage. The preprocessing stage of the proposed method detects and reduces the noise contents in the source ultra sound thyroid images. Then, the texture features are computed from the preprocessed ultra sound thyroid image. Finally, these features are diagnosed into normal, mild and severe case using spatial Fuzzy-C-Mean classification (SFCM) approach. This method is tested on the ultra sound thyroid images in both DDTI and Open-CAS dataset with respect to accuracy, precision, recall and diagnosis rate
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