As macroscopic Breast Cancer (BC) imaging diagnostics do not provide satisfied data, microscopic image analysis is used to get optimum cell-nuclei segmentation for image categorization. The paper presents a Novel Hybrid Segmentation Method which uses the energy detail coefficients attained from Non-Subsampled Contourlet Transform for cell-nuclei segmentation of BC histopathological images. NHSM solves the local inhomogeneities problem while preserving all necessary details. The system evaluation is done using training dataset of 98 BC histopathological images and testing dataset of 26 images. Total of 25 segmented features was extracted for classification using Decision Trees, k-Nearest Neighbor, multilayer perceptron, Multiclass-Support Vector Machine (M-SVM). The proposed NHSM segmentation results were undergone through both subjective and objective performance evaluation. Result evaluation proved that NHSM along with Multi Class-Support Vector Machine classifier provides highly precise and accurate classified result for BC tissue images compared to state-of-the-art segmentation methods. Also, the edge detection model initialization is highly robust and insensitive.

由于宏观乳腺癌 (BC) 成像诊断无法提供满意的数据，因此使用显微图像分析来获得最佳的细胞核分割以进行图像分类。本文提出了一种新型混合分割方法，该方法使用从非下采样轮廓波变换获得的能量细节系数对 BC 组织病理学图像进行细胞核分割。NHSM 解决了局部不均匀性问题，同时保留了所有必要的细节。系统评估是使用 98 个 BC 组织病理学图像的训练数据集和 26 个图像的测试数据集完成的。使用决策树、k 最近邻、多层感知器、多类支持向量机 (M-SVM) 提取总共 25 个分段特征进行分类。所提出的 NHSM 分割结果是通过主观和客观性能评估进行的。结果评估证明，与最先进的分割方法相比，NHSM 与多类支持向量机分类器一起为 BC 组织图像提供了高精度和准确的分类结果。此外，边缘检测模型初始化具有高度鲁棒性且不敏感。