A method for generating context-aware features for object classification and its application to IHC stained image analysis

Tissue object classification, such as nuclei classification, is often the pre-requisite for quantitative analysis of histopathological tissue slide images. However, inter-image variations, including biological and stain variations, impose great challenges for robust classification. While biological variations have yet been addressed explicitly, stain variation issues are mainly solved by aligning color distribution to improve global stain appearance consistency. Such methods are risky when classification needs to be performed among objects differing by subtle color differences, as image color distributions are also affected by objects prevalence in a given image. In this paper, we present a simple yet effective method that incorporates object and context features to implicitly compensate for the inter-image variations. The basic idea is to first identify object features that characterize the objects well within the image. Then, for each object feature, identify a set of associated context features that characterize the feature variations between the images. Finally, each object feature and the associated context features are used to train a base classifier, which generates a numeric value representing the degree to which an object belongs to a class. This value is called the “context-aware” feature and the input to the end classifier. The proposed method is used to address classification of lymphocytes and negatively expressed tumor cells in immunohistochemical (IHC) stained breast cancer images. Experiments show significant descriptive power gain of the features which also boost the end classifier performance.