Image processing of hematoxylin and eosin-stained tissues for pathological evaluation

Color and geometric characteristics of stained areas in histochemical slides are among the features pathologists assess to evaluate the severity of lesions. In this research, image processing techniques were used to perform objective quantification of these characteristics in images of H&E-stained spleen tissues. A segmentation algorithm was developed to isolate the areas of interest in microscopic tissue images. Image features important to pathological evaluation were then extracted. These features were used to build statistical and neural network models to predict pathologist scores. A linear regression model predicted the scores to an R-2-value of 0.6, and a neural network model classified samples to an accuracy of 75%. The results show the usefulness of image processing as a tool for pathological evaluation.