<title>Data fusion for detection of early-stage lung cancer cells using evidential reasoning</title>

Abstract

Data fusion has already been widely used in various applications in which multiple sources of information are presented. One of the most widely used applications of data fusion is in the field of object recognition and classification, since it can efficiently improve the accuracy and the ability of fault tolerance. This presentation describes a cytological color image processing system, using data fusion method, developed for the detection of early stage lung cancers used in the health inspection. As most of the existing microscopic diagnostic systems use morphological, textual and gray or color features respectively, which results in the instability of the diagnosis, this system makes use of all these features by fusing multiple classification results obtained using morphological, textual and chromatic features respectively. Data fusion is achieved using Dempster-Shafer's Evidential Reasoning (DSER). In the current system, all the nuclei are first segmented by thresholding in a special color space which is a non-linear transformation of the (R,G,B) color space. Then, using morphological, textual and chromatic features respectively, the segmented cells are classified as normal or abnormal cells. Using DSER, the classification results obtained above are fused into a final result. Finally, a decision strategy based on the fused data is presented to get the final classification results. And experiment results are given to show the feasibility of the data fusion approach proposed here.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.