Computer-Assisted Diagnosis of Lung Cancer Using Quantitative Topology Features
In this paper, we proposed a computer-aided diagnosis and analysis for a challenging and important clinical case in lung cancer, i.e., differentiation of two subtypes of Non-small cell lung cancer (NSCLC). The proposed framework utilized both local and topological features from histopathology images. To extract local features, a robust cell detection and segmentation method is first adopted to segment each individual cell in images. Then a set of extensive local features is extracted using efficient geometry and texture descriptors based on cell detection results. To investigate the effectiveness of topological features, we calculated architectural properties from labeled nuclei centroids. Experimental results from four popular classifiers suggest that the cellular structure is very important and the topological descriptors are representative markers to distinguish between two subtypes of NSCLC.