An Interactive Statistical Image Segmentation and Visualization System

Supervised learning can be used to segment /identify regions of interest in images making use of color and morphological information. A novel object identification algorithm was developed in Java to locate immune and cancer cells in images of immunohistochemically-stained lymph node tissue from the recent Kohrt study[1] and also shows promise in other domains. Our method leans heavily on the use of color and the relative homogeneity of object appearance. As is often the case in segmentation, an algorithm specifically tailored to the application works better than using broader methods that work passably well on any problem. Our main innovation is interactive feature extraction from color images. We also enable the user to improve the classification with an interactive visualization system. This is then coupled with the statistical learning algorithms and intensive interaction with the user over many classification-correction iterations, resulting in a highly accurate and user-friendly solution. The system ultimately provides the locations of every cell in the entire tissue. This data can be analyzed using standard statistical methods such as spatial analyses or robust clustering. This data is invaluable in the study of multidimensional relationships between cell populations and tumor structure.