A bottom-up approach for tumour differentiation in whole slide images of lung adenocarcinoma

Analysis of tumour cells is essential for morphological characterisation which is useful for disease prognosis and survival prediction. Visual assessment of tumour cell morphology by expert human observers for prognostic purposes is subjective and potentially a tedious process. In this paper, we propose an automated and objective method for tumour cell analysis in whole slide images (WSI) of lung adenocarcinoma. Tumour cells are first extracted at higher magnification and then morphological, texture and spatial distribution features are computed for each cell. We investigated the biological impact of the nuclear features in the context of tumour grading. Results show that some of these features are correlated with tumour grade. We examine some of these features on the WSI where these features shows different distribution depends on the tumour grade.