Importance of image quality for computing texture features in biomedical specimens
Branko Palcic, Bruno Jaggi, Calum E. MacAulay · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1990
Quantitative descriptions of features offer an objective cell classification. In addition, quantitative analysis is ideally suited for automated screening of samples for diseased (transformed) cells. More recently, it has been shown that subtle changes of DNA distribution in the nucleus can be detected by quantitative analysis which escape human detection . A key issue in quantitative analysis is the quality of the digital image which must be captured for such analysis . This paper focuses on the affect of image quality on the very important features--texture features--which have been shown to be the most powerful classifiers in quantitative pathology . Image quality affects these features in a direct and indirect way. The latter is the result of an effect on texture features by the segmentation process which in turn strongly depends on image quality.