Differentiating between Benign and Malignant non-Mass Enhancing Lesions in Breast DCE-MRI by Using Curvelet-based Textural Features

机译：使用基于Curvelet的纹理特征区分乳腺DCE-MRI良性和恶性非肿块

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Breast DCE-MR imaging plays an important role in effective detection and diagnosis of breast cancer. Non-mass enhancing breast lesions have been less studied in CADx systems because of their challenging intrinsic. In this study, a CADx system is proposed for differentiating benign and malignant non-mass enhancing lesions in breast DCE-MRI. Proposed system uses dynamic information of the 4D DCE-MRI data to segment the lesions on the basis of a fuzzy clustering algorithm. Curvelet-based textural features are extracted from 3D segmented lesions and classified by SVM classifier. The results achieved the accuracy of 75% and AUC of 0.75 for non-mass enhancing breast lesions which provides comparable results to other recent methods.

机译：乳腺癌DCE-MR成像在有效检测和诊断乳腺癌中起着重要作用。由于CADx系统固有的挑战性，非质量增强型乳腺病变的研究较少。在这项研究中，提出了一种CADx系统，用于区分乳腺DCE-MRI中的良性和恶性非肿块增强病变。所提出的系统基于模糊聚类算法，利用4D DCE-MRI数据的动态信息对病变进行分割。从3D分割的病变中提取基于Curvelet的纹理特征，并通过SVM分类器对其进行分类。对于非肿块增强的乳腺病变，结果达到了75％的准确度和0.75的AUC，与其他最近的方法可比。

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来源
《Iranian Conference on Signal Processing and Intelligent Systems》|2018年|101-105|共5页
会议地点 Tehran(IR)
作者
Fazael Ayatollahi; Parinaz Eskandari; Shahriar B. Shokouhi;
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作者单位

Electrical Engineering Department Iran University of Science and Technology Tehran Iran;

Electrical Engineering Department Iran University of Science and Technol;

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Lesions; Breast; Feature extraction; Transforms; Cancer; Support vector machines; Kinetic theory;

机译：病变；乳房;特征提取;转换；癌症;支持向量机；动力学理论;

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专利

1. Texture features and pharmacokinetic parameters in differentiating benign and malignant breast lesions by dynamic contrast enhanced magnetic resonance imaging [J] . Niu Qingliang, Jiang Xiaomei, Li Qin, Oncology letters . 2018,第4aPta1期

机译：通过动态对比度增强型磁共振成像来区分良性和恶性乳房病变的纹理特征和药代动力学参数
2. Apparent diffusion coefficient values as an adjunct to dynamic contrast enhanced MRI for discriminating benign and malignant breast lesions presenting as mass and non-mass like enhancement [J] . Fatma Zeinhom Moukhtar, Amal Amin Abu El Maati The Egyptian Journal of Radiology and Nuclear Medicine . 2014,第2期

机译：视扩散系数值可作为动态对比增强MRI的辅助手段，用于区分以肿块和非肿块样增强表现的良性和恶性乳腺病变
3. Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women [J] . Yu Tan, Hui Mai, Zhiqing Huang, BMC Medical Imaging . 2021,第1期

机译：基于乳房MRI的纹理分析的附加价值区分良性和恶性非群体在绝育女性中的良性和恶性非群体增强
4. Differentiating between Benign and Malignant non-Mass Enhancing Lesions in Breast DCE-MRI by Using Curvelet-based Textural Features [C] . Fazael Ayatollahi, Parinaz Eskandari, Shahriar B. Shokouhi Iranian Conference of Signal Processing and Intelligent Systems . 2018

机译：利用基于曲线的纹理特征来区分乳腺DCE-MRI中的良性和恶性非质量增强病变
5. Differentiating benign and malignant inflammatory breast lesions: Value of T2 weighted and diffusion weighted MR images [D] . Kanao, Shotaro 2019

机译：区分乳腺良性和恶性炎症病变：T2加权和弥散加权MR图像的价值
6. Texture features and pharmacokinetic parameters in differentiating benign and malignant breast lesions by dynamic contrast enhanced magnetic resonance imaging [O] . Qingliang Niu, Xiaomei Jiang, Qin Li, -1

机译：动态对比增强磁共振成像鉴别乳腺良恶性病变的质地特征和药代动力学参数
7. Apparent diffusion coefficient values as an adjunct to dynamic contrast enhanced MRI for discriminating benign and malignant breast lesions presenting as mass and non-mass like enhancement [O] . Fatma Zeinhom Moukhtar, Amal Amin Abu El Maati 2014

机译：表观弥散系数值作为动态对比增强mRI的辅助手段，用于区分良性和恶性乳腺病变，表现为质量和非质量增强

Differentiating between Benign and Malignant non-Mass Enhancing Lesions in Breast DCE-MRI by Using Curvelet-based Textural Features

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