A CAD mitosis detection system from breast cancer histology images based on fused features

Nowadays, automatic computer-Aided Diagnosis (CAD) systems for grading different types of cancers like breast cancer are very prevalent. These systems employ histopathology slide images acquired by advanced and well-defined digital scanners. The previously proposed automatic or computer-aided systems for breast cancer grading, especially by counting mitoses, suffer from various types of deficiencies. The most important one is their low efficiency along with high complexity due to the huge amount of features. In this paper, two types of features with more flexibility and less complexity are employed. These features are Completed Local Binary Pattern (CLBP) as textural features and Stiffness Matrix as geometric, morphometric and shape-based features. In the proposed automatic mitosis detection system, these two features are fused with each other. The evaluation results are for histology Dataset H (Hamamatsu Nanozoomer Scanners) provided by Mitos-ICPR2012 contest sponsors. Employing a nonlinear RBF kernel support vector machine (SVM) classifier with parameter sigma which equals to 100, leads to an efficiency of 82%. The results are in the form of F-measure criterion which is a reliable and mostly common evaluation criterion for such biological systems.