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首页> 外文期刊>Medical hypotheses >Brain tumor classification using modified local binary patterns (LBP) feature extraction methods
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Brain tumor classification using modified local binary patterns (LBP) feature extraction methods

机译：脑肿瘤分类使用改进的局部二进制图案（LBP）特征提取方法

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摘要

Automatic classification of brain tumor types is very important for accelerating the treatment process, planning and increasing the patient's survival rate. Today, MR images are used to determine the type of brain tumor. Manual diagnosis of brain tumor type depends on the experience and sensitivity of radiologists. Therefore, researchers have developed many brain tumor classification models to minimize the human factor. In this study, two different feature extraction (nLBP and alpha LBP) approaches were used to classify the most common brain tumor types; Glioma, Meningioma, and Pituitary brain tumors. nLBP is formed based on the relationship for each pixel around the neighbors. The nLBP method has a d parameter that specifies the distance between consecutive neighbors for comparison. Different patterns are obtained for different d parameter values. The alpha LBP operator calculates the value of each pixel based on an angle value. The angle values used for calculation are 0, 45, 90 and 135. To test the proposed methods, it was applied to images obtained from the brain tumor database collected from Nanfang Hospital, Guangzhou, China, and Tianjin Medical University General Hospital between the years of 2005 and 2010. The classification process was performed by using K-Nearest Neighbor (Knn) and Artificial Neural Networks (ANN), Random Forest (RF), A1DE, Linear Discriminant Analysis (LDA) classification methods, with the feature matrices obtained with nLBP, alpha LBP and classical LBP from the images in the data set. The highest success rate in brain tumor classification was 95.56% with the nLBPd = 1 feature extraction method and Knn model.
机译：脑肿瘤类型的自动分类对于加速治疗过程，规划和增加患者的生存率非常重要。如今，MR图像用于确定脑肿瘤的类型。手动诊断脑肿瘤类型取决于放射科医生的经验和敏感性。因此，研究人员已经开发出许多脑肿瘤分类模型，以最大限度地减少人类因素。在该研究中，使用两种不同的特征提取（NLBP和α1BP）方法来分类最常见的脑肿瘤类型;胶质瘤，脑膜瘤和垂体脑肿瘤。基于邻居周围的每个像素的关系形成NLBP。 NLBP方法具有D参数，该D参数指定连续邻居之间的距离进行比较。针对不同的D参数值获得不同的模式。 Alpha LBP操作员基于角度值计算每个像素的值。用于计算的角度值为0,45,90和135.为了测试所提出的方法，它应用于从多年来广州，广州，中国和天津医科大学综合医院收集的脑肿瘤数据库中获得的图像2005年和2010年。通过使用K-CORMALY邻（KNN）和人工神经网络（ANN），随机林（RF），A1DE，线性判别分析（LDA）分类方法进行分类过程，其中具有与之相处的特征矩阵NLBP，Alpha LBP和数据集中图像中的古典LBP。 NLBPD = 1特征提取方法和KNN模型，脑肿瘤分类的最高成功率为95.56％。

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