Detection of regions of interest's in mammograms by using local binary pattern, dynamic k-means algorithm and gray level co-occurrence matrix

机译：使用局部二进制模式，动态k均值算法和灰度共生矩阵来检测乳房X线照片中的感兴趣区域

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This paper presents a method for the detection of the regions of interest's (ROIs) in mammograms by using dynamic k-means clustering algorithm. In this approach, a method has been developed to determine the initialization number of clusters in mammograms by using a data mining algorithm based on the Local Binary Pattern (LBP) and co-occurrence matrix technique (GLCM). Our method consists of three phases: firstly preprocessing images by using Thresholding and filtering methods; secondly determining the initialization number of clusters in mammography images; thirdly detecting of regions of interest's (ROIs) in mammography images. The proposed method was tested using data from Mini-MIAS (Mammogram Image Analysis Society, UK) database, consisting of 322 mammograms. The results from the tests confirm the effectiveness of the proposed method the determination number of clusters and detected of Regions of interest's (ROIs) in mammography images.

机译：本文提出了一种使用动态k均值聚类算法检测乳房X线照片中感兴趣区域（ROI）的方法。在这种方法中，已经开发出一种方法，该方法通过使用基于局部二进制模式（LBP）和共现矩阵技术（GLCM）的数据挖掘算法来确定乳房X线照片中簇的初始化数量。我们的方法包括三个阶段：首先使用阈值和过滤方法对图像进行预处理;第二，对图像进行预处理。其次确定乳腺X射线摄影图像中簇的初始化数目。第三，在乳腺X射线摄影图像中检测感兴趣区域（ROI）。使用Mini-MIAS（英国乳房X线图像分析协会）数据库中的数据进行了测试，该数据库由322个乳房X线照片组成。测试的结果证实了所提出方法的有效性，确定了乳腺X线摄影图像中的簇数并检测了感兴趣区域（ROI）。

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《International Conference on Next Generation Networks and Services》|2014年|118-123|共6页
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Elmoufidi Abdelali; Fahssi Khalid El; Jai-Andaloussi Said; Madrane Nabil; Sekkaki Abderrahim;
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Breast cancer; Classification algorithms; Clustering algorithms; Databases; Heuristic algorithms; Histograms; Breast Cancer; Gray Level Cooccurrence Matrix (GLCM); K-means algorithm; Local Binary Pattern (LBP); Mammography Images; Regions of Interest's (ROIs);

机译：乳腺癌;分类算法;聚类算法;数据库;启发式算法;直方图;乳腺癌;灰度级共生矩阵（GLCM）; K均值算法;局部二值模式（LBP）;乳腺摄影图像;感兴趣区域（ROI）;

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3. Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer [J] . Shradhananda Beura, Banshidhar Majhi, Ratnakar Dash Neurocomputing . 2015,第apra22期

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Detection of regions of interest's in mammograms by using local binary pattern, dynamic k-means algorithm and gray level co-occurrence matrix

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