Objectives

Breast cancer (BC) is one of the most commonly reported health issues worldwide, especially in females. Early detection and diagnosis of BC can greatly reduce mortality rates. Samples obtained with different imaging methods such as mammography, computerized tomography, magnetic resonance, ultrasound, and biopsy are used in the diagnosis of BC. Histopathological images obtained from a biopsy contain vital information about the stage of the BC. Computer-aided systems are important tools to assist pathologists in the early detection of BC.

Material and methods

In the current study, the use of gray-level co-occurrence matrix (GLCM) of Shearlet Transform (ST) coefficients were first scrutinized as textural features. ST is an advanced decomposition-based method that can analyze images in various directions and is sensitive to edge singularities. These features make ST more robust than other decomposition methods such as Fourier and wavelet. Color channel histogram features were also utilized for a second level of evaluation in the diagnosis of the BC stage. These features are considered one of the most important building blocks that pathologists consider in the course of grading histopathological images. Then, by combining these two features, the classification results were re-assessed utilizing Support Vector Machine (SVM) as a classifier.

Results

The assessments were performed on a BreaKHis dataset containing benign and malignant histopathological samples. The average accuracy scores were reported as being 98.2%, 97.2%, 97.8%, and 97.3% in the sub-databases with 40×, 100×, 200×, and 400× magnification factors, respectively.

Conclusions

The obtained results showed that the proposed method was quite efficient in histopathological image classification. Despite the relative simplicity of the approach, the obtained results were far superior to previously reported results.

中文翻译：

---

利用同时发生的Shearlet变换和直方图特征诊断乳腺癌的自动评分系统

目标

乳腺癌（BC）是全世界最普遍报道的健康问题之一，尤其是女性。BC的早期发现和诊断可以大大降低死亡率。通过不同的成像方法（如乳腺X线摄影，计算机断层扫描，磁共振，超声和活检）获得的样本可用于诊断BC。从活检获得的组织病理学图像包含有关BC阶段的重要信息。计算机辅助系统是协助病理学家及早发现BC的重要工具。

材料与方法

在当前的研究中，Shearlet变换（ST）系数的灰度共现矩阵（GLCM）的使用首先被考察为纹理特征。ST是一种基于分解的高级方法，可以分析各个方向的图像，并且对边缘奇异点敏感。这些特征使ST比其他分解方法（如Fourier和Wavelet）更健壮。彩色通道直方图特征还用于BC阶段诊断的第二级评估。这些特征被认为是病理学家在对组织病理学图像进行分级的过程中考虑的最重要的组成部分之一。然后，通过结合这两个特征，使用支持向量机（SVM）作为分类器对分类结果进行重新评估。

结果

对包含良性和恶性组织病理学样本的BreaKHis数据集进行了评估。据报道，在具有40倍，100倍，200倍和400倍放大倍数的子数据库中，平均准确性得分分别为98.2％，97.2％，97.8％和97.3％。

结论

所得结果表明，该方法在组织病理图像分类中非常有效。尽管该方法相对简单，但获得的结果仍远远优于先前报道的结果。