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 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 region...

• Type: Conference
• Year: 2002
• Where: KDD
• Authors: Bin Fang, Wynne Hsu, Mong-Li Lee
• Keywords: Data Mining, Extracted Features Rules, KDD 2002, Tumor Cell Counting, Tumour Cell Counting