Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/43192
Title: Tumor cell identification using features rules
Authors: Ang, B. 
Hsu, W. 
Lee, M.L. 
Keywords: Dynamic water immersion
Features rules
Identification
Local adaptive thresholding
Majority vote
Meta classifier
Weighted vote
Issue Date: 2002
Source: Ang, B.,Hsu, W.,Lee, M.L. (2002). Tumor cell identification using features rules. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : 495-500. ScholarBank@NUS Repository.
Abstract: 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 tumour cell counting. The quantitative analysis of tumour cells is fundamental to characterizing the activity of tumour 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.
Source Title: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
URI: http://scholarbank.nus.edu.sg/handle/10635/43192
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