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Multi-view breast thermogram analysis by fusing texture features
Quantitative InfraRed Thermography Journal ( IF 2.5 ) Pub Date : 2019-04-29 , DOI: 10.1080/17686733.2018.1544687
Vijaya Madhavi 1 , Christy Bobby Thomas 2
Affiliation  

Breast cancer is the prevalent cancer type in women and thermography aids in premature detection by utilising ultra-sensitive infrared cameras. In this work, normal and abnormal thermograms are differentiated by extracting and fusing texture features from frontal and lateral views. Multi-view thermograms are pre-processed using anisotropic diffusion. The Region of Interest from axilla to lower breast boundary is extracted through level-set segmentation without re-initialisation. Texture features such as grey-level co-occurrence matrix, grey-level run length matrix, grey-level size zone matrix and neighbourhood grey tone difference matrix that quantitatively describe local or regional texture properties are extracted for 32-normal and 31-abnormal subjects chosen from DMR database. Using t-test, the reduced feature set is determined for frontal, right-lateral and left-lateral thermograms independently from the extracted texture features. Significant features are obtained by performing kernel principal component analysis on the reduced feature set. Feature fusion is performed on obtained significant features from frontal and lateral views to obtain a composite feature vector that is fed to least square-support vector machine employing optimised hyper-parameters to classify subjects as normal and abnormal. Experimental results indicate that fusion of texture features from frontal and lateral thermograms achieved 96% accuracy, 100% sensitivity and 92% specificity.



中文翻译:

通过融合纹理特征进行多视图乳房温度分析

乳腺癌是女性中常见的癌症类型,热成像技术可通过使用超灵敏的红外热像仪来帮助提早发现。在这项工作中,通过从正面和侧面提取和融合纹理特征来区分正常和异常温度记录图。使用各向异性扩散对多视图温度记录图进行预处理。通过水平集分割提取从腋窝到乳房下限的感兴趣区域,而无需重新初始化。针对32个正常和31个异常对象提取了纹理特征,例如灰度共现矩阵,灰度游程长度矩阵,灰度大小带矩阵和邻域灰度差异矩阵,这些特征定量描述了局部或区域纹理特性从DMR数据库中选择。使用t检验,可确定正面的简化特征集,独立于提取的纹理特征的右侧和左侧温度记录图。通过对精简特征集执行内核主成分分析,可以获得重要特征。从正面和侧面对获得的重要特征执行特征融合,以获得复合特征向量,该特征向量被馈送到采用优化超参数将对象分类为正常和异常的最小二乘支持向量机。实验结果表明,来自正面和侧面的热像图的纹理特征融合达到了96%的准确度,100%的灵敏度和92%的特异性。从正面和侧面对获得的重要特征执行特征融合,以获得复合特征向量,该特征向量被馈送到采用优化超参数将对象分类为正常和异常的最小二乘支持向量机。实验结果表明,正面和侧面温度图的纹理特征融合达到了96%的准确度,100%的灵敏度和92%的特异性。从正面和侧面对获得的重要特征执行特征融合,以获得复合特征向量,该特征向量被馈送到采用优化超参数将对象分类为正常和异常的最小二乘支持向量机。实验结果表明,来自正面和侧面的热像图的纹理特征融合达到了96%的准确度,100%的灵敏度和92%的特异性。

更新日期:2019-04-29
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