Breast cancer histopathological images recognition based on two-stage nuclei segmentation strategy

Abstract

Pathological examination is the gold standard for breast cancer diagnosis. The recognition of histopathological images of breast cancer has attracted a lot of attention in the field of medical image processing. In this paper, on the base of the Bioimaging 2015 dataset, a two-stage nuclei segmentation strategy, that is, a method of watershed segmentation based on histopathological images after stain separation, is proposed to make the dataset recognized to be the carcinoma and non-carcinoma recognition. Firstly, stain separation is performed on breast cancer histopathological images. Then the marker-based watershed segmentation method is used for images obtained from stain separation to achieve the nuclei segmentation target. Next, the completed local binary pattern is used to extract texture features from the nuclei regions (images after nuclei segmentation), and color features were extracted by using the color auto-correlation method on the stain-separated images. Finally, the two kinds of features were fused and the support vector machine was used for carcinoma and non-carcinoma recognition. The experimental results show that the two-stage nuclei segmentation strategy proposed in this paper has significant advantages in the recognition of carcinoma and non-carcinoma on breast cancer histopathological images, and the recognition accuracy arrives at 91.67%. The proposed method is also applied to the ICIAR 2018 dataset to realize the automatic recognition of carcinoma and non-carcinoma, and the recognition accuracy arrives at 92.50%.

Toward More Transparent and Accurate Cancer Diagnosis With an Unsupervised CAE Approach

“…A binary threshold is set to detect the contour of the extracted nuclei in the images, as the morphological characteristics of the cells are critical to grading the cancers. Moreover, the two-stage nuclei segmentation strategy proposed in [9] based on watershed segmentation is used to distinguish between carcinoma and non-carcinoma recognition in the Bio-imaging 2015 data set. Additionally, [10] introduced a novel approach to detect nuclei in breast cancer histopathological images using a stacked sparse Auto Encoder (AE).…”