document: Tumor cell identification using features rules Advances in imaging techniques have led to large repositories of images . There is an increasing demand for automated systems that can analyze complex medical images and extract meaningful information for mining patterns . Here , we describe a real-life image mining application to the problem of tumor cell counting . The quantitative analysis of tumor cells is fundamental to characterizing the activity of tumor cells . Existing approaches are mostly manual , time-consuming and subjective . Efforts to automate the process of cell counting have largely focused on using image processing techniques only . Our studies indicate that image processing alone is unable to give accurate results . In this paper , we examine the use of extracted features rules to aid in the process of tumor cell counting . We propose a robust local adaptive thresholding and dynamic water immersion algorithms to segment regions of interesting from background . Meaningful features are then extracted from the segmented regions . A number of base classifiers are built to generate features rules to help identify the tumor cell . Two voting strategies are implemented to combine the base classifiers into a meta-classifier . Experiment results indicate that this process of using extracted features rules to help identify tumor cell leads to better accuracy than pure image processing techniques alone .
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