In the medical field, image segmentation and classification possesses high worth in disease diagnosis and grading. The proposed novel technique segments and classifies histopathology images to find the nuclei pattern for disease diagnosis in four steps: (1) preprocessing, (2) segmentation, (3) feature extraction, and (4) classification. The image is preprocessed through a circular kernel and thresholding followed by curve fitting-based segmentation. The method employs image classification by support vector machine using extraction of bag of visual words and handcrafted features from original and segmented image, respectively. The performance evaluation of the proposed method is done for H&E-stained histopathology images on seven datasets: ADL, Bisque, BreakHis, Nuclei, MoNuSeg 2018, DSB2018, and the proposed MAHIBT. The classification result reports the superiority of the proposed method on the basis of accuracy and area under the characteristics curve, as the existing methods. The weighted cumulation factor confirms the supremacy of the proposed method in segmentation and classification for complex histopathology images.

在医学领域，图像分割和分类在疾病诊断和分级中具有很高的价值。所提出的新技术通过四个步骤对组织病理学图像进行分割和分类，以找到用于疾病诊断的细胞核模式：（1）预处理，（2）分割，（3）特征提取和（4）分类。图像通过圆形内核和阈值进行预处理，然后是基于曲线拟合的分割。该方法采用支持向量机进行图像分类，分别从原始图像和分割图像中提取视觉词袋和手工特征。在七个数据集上对 H&E 染色的组织病理学图像进行了所提出方法的性能评估：ADL、Bisque、BreakHis、Nuclei、MoNuSeg 2018、DSB2018 和提议的 MAHIBT。分类结果在精度和特征曲线下面积的基础上报告了所提出的方法与现有方法的优越性。加权累积因子证实了所提出的方法在复杂组织病理学图像的分割和分类中的优越性。