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Analysis of nuclei textures of fine needle aspirated cytology images for breast cancer diagnosis using Complex Daubechies wavelets

机译:复杂Daubechies小波分析细针吸取细胞学图像的核纹理以诊断乳腺癌

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摘要

Breast cancer is the most frequent cause of cancer induced death among women in the world. Diagnosis of this cancer can be done through radiological, surgical, and pathological assessments of breast tissue samples. A common test for detection of this cancer involves visual microscopic inspection of Fine Needle Aspiration Cytology (FNAC) samples of breast tissue. The result of analysis on this sample by a cyto-pathologist is crucial for the breast cancer patient. For the assessment of malignancy, the chromatin texture patterns of the cell nuclei are essential. Wavelet transforms have been shown to be good tools for extracting information about texture. In this paper, it has been investigated whether complex wavelets can provide better performance than the more common real valued wavelet transform. The features extracted through the wavelets are used as input to a k-nn classifier. The correct classification results are obtained as 93.9% for the complex wavelets and 70.3% for the real wavelets.
机译:在世界范围内,乳腺癌是导致癌症死亡的最常见原因。可以通过对乳房组织样本进行放射,外科和病理评估来诊断这种癌症。对该癌症进行检测的常见测试涉及对乳腺组织的细针穿刺细胞学检查(FNAC)样品进行目镜检查。细胞病理学家对该样品的分析结果对于乳腺癌患者至关重要。为了评估恶性,细胞核的染色质质地模式是必不可少的。小波变换已被证明是提取纹理信息的良好工具。在本文中,已经研究了复数小波是否可以提供比更普通的实值小波变换更好的性能。通过小波提取的特征用作k-nn分类器的输入。正确的分类结果对于复数子波为93.9%,对实数子波为70.3%。

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