It is common to analyze and diagnose breast cancer (BC) via HE-stained BC histopathological images. Pathologists generally believe that the pathological and morphological features of tumor nests (TNs) and stroma indicate biological behavior of BC, so it becomes particularly important to accurately segment the TNs and stroma. For HE-stained BC histopathology images, we regard the segmentation as the classification of pixels in the image, extract and analyze features, choose the best combination of features, and then classify it as TNs or stroma. And procedures of sample interval, normalization and thresholding are also taken into account. Experimental results show that the proposed method can segment the TNs and stromata accurately and ensure higher accuracy and precision. What's more, it is satisfactory in terms of time efficiency.