Breast Tissues Classification Based on the Application of Geostatistical Features and Wavelet Transform

G. Bráz, E. C. da Silva, A.C. de Paiva, A.C. Silva
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引用次数: 10

Abstract

Female breast cancer is the major cause of death in occidental countries. Efforts in computer vision have been made in order to help improving the diagnostic accuracy by radiologists. We propose a methodology to distinguish Mass and Non-Mass tissues on mammograms. It is based on the computation of geostatistical measures (Moran's Index and Geary's Coefficient) over a multiresolution image representation trough wavelet transform. The computed measures are classified through a Support Vector Machine (SVM). The methodology reaches 98.36% of Specificity, 98.13% of Sensitivity and a rate of 98.24% to discriminate Mass from Non-Mass elements, using the Geary's Coefficient application.
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基于地统计特征和小波变换的乳腺组织分类
女性乳腺癌是西方国家的主要死亡原因。为了帮助提高放射科医生的诊断准确性,人们在计算机视觉方面做出了努力。我们提出了一种方法来区分肿块和非肿块组织的乳房x线照片。它是基于小波变换对多分辨率图像表示的地质统计度量(Moran指数和Geary系数)的计算。计算得到的测度通过支持向量机(SVM)进行分类。应用Geary’s系数,该方法鉴别质量与非质量元素的特异性为98.36%,灵敏度为98.13%,鉴别率为98.24%。
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