ABSTRACT
A system has been developed that combines multiparameter fluorescence imaging and computer vision techniques to provide automatic phenotyping of multiple cell types in a single tissue section. This system identifies both the nuclear and cytoplasmic boundary of each cell. A routine based on the watershed algorithm has been developed to segment an image of Hoechst-stained nuclei with an accuracy of greater than 85%. Deformable splines initially positioned at the nuclear boundaries are applied to images of fluorescently labelled cell-surface antigens. The splines lock onto the peak fluorescence signal surrounding the cell, providing an estimate of the cell boundary. From measurements acquired at this boundary, each cell is classified according to antigen expression. The system has been piloted in biopsies from melanoma patients participating in a clinical trial of the antibody R24. Thin tissue sections have been stained with Hoechst and three different fluorescent antibodies to antigens that permit the typing and evaluation of activity of T-cells. Changes in the infiltrates evaluated by multiparameter imaging were consistent with results obtained by immunoperoxidase analysis. The multiparameter fluorescent technique enables simultaneous determination of multiple cell subsets and can provide the spatial relationships of each cell type within the tissue.
Subject(s)
Image Processing, Computer-Assisted/methods , Immunophenotyping/methods , Lymphocyte Activation , Lymphocyte Count/methods , Lymphocyte Subsets , Lymphocytes, Tumor-Infiltrating , Melanoma/pathology , Microscopy, Fluorescence/methods , Antigens, CD/analysis , Automation , Biopsy , Bisbenzimidazole , Color , Fluorescent Dyes , HLA-DR Antigens/analysis , Humans , Image Processing, Computer-Assisted/instrumentation , Immunoenzyme Techniques , Immunophenotyping/instrumentation , Lymphocyte Count/instrumentation , Lymphocyte Subsets/chemistry , Lymphocyte Subsets/immunology , Lymphocytes, Tumor-Infiltrating/chemistry , Lymphocytes, Tumor-Infiltrating/immunology , Melanoma/immunology , Microscopy, Fluorescence/instrumentation , Pilot ProjectsABSTRACT
Segmenting 3D textured surfaces is critical for general image understanding. Unfortunately, current efforts of automatically understanding image texture are based on assumptions that make this goal impossible. Texture-segmentation research usually assumes that the textures are flat and viewed from the front, while shape-from-texture work assumes that the textures have already been segmented. This deadlock means that none of these algorithms will work reliably on images of 3D textured surfaces. An algorithm has been developed by the authors that can segment an image containing nonfrontally viewed, planar, periodic textures. The spectrogram (local power spectrum) is used to compute local surface normals from small regions of the image. This algorithm does not require unreliable image feature detection. Based on these surface normals, a 'frontalized' version of the local power spectrum is computed that shows what the region's power spectrum would look like if viewed from the front. If neighboring regions have similar frontalized power spectra, they are merged. The merge criterion is based on a description length formula. The segmentation is demonstrated on images with real textures. To the authors' knowledge, this is the first program that can segment 3D textured surfaces by explicitly accounting for 3D shape effects.
