Histopathological Image Analysis: A Review <s> A. Color Normalization <s> One of the most commonly used clinical tests performed today is the routine evaluation of peripheral blood smears. In this paper, we investigate the design, development, and implementation of a robust color gradient vector flow (GVF) active contour model for performing segmentation, using a database of 1791 imaged cells. The algorithms developed for this research operate in Luv color space, and introduce a color gradient and L/sub 2/E robust estimation into the traditional GVF snake. The accuracy of the new model was compared with the segmentation results using a mean-shift approach, the traditional color GVF snake, and several other commonly used segmentation strategies. The unsupervised robust color snake with L/sub 2/E robust estimation was shown to provide results which were superior to the other unsupervised approaches, and was comparable with supervised segmentation, as judged by a panel of human experts. <s> BIB001 </s> Histopathological Image Analysis: A Review <s> A. Color Normalization <s> Two common imaging modalities for histological sections are brightfield and fluorescence microscopy imaging. Hematoxylin-Eosin (H&E) based brightfield microscopy has been the traditional imaging technique for imaging morphology, while an epi-fluorescent microscope is used for immunofluorescent staining of specific proteins or fluorescent in situ hybridization (FISH) for genetic based analysis of DNA. Simultaneous imaging of both microscopy modalities has been difficult due to optical and chemical effects of the H&E dyes. We present a novel sequential imaging and registration technique that enables brightfield and fluorescent imaging on the same tissue section, hence combining the traditional anatomic pathology with the newly emerging field of molecular pathology. First the tissue is labeled with fluorescent biomarkers, and imaged through a fluorescence microscope, and then the tissue is re-labeled with H&E dyes, and imaged again with traditional brightfield. Our robust registration algorithms achieve 99.8% registration success rate on tissue micro array (TMA) sections. <s> BIB002
One of the first steps essential for both fluorescent and bright field microscopy image analysis is color and illumination normalization. This process reduces the differences in tissue samples due to variation in staining and scanning conditions. The illumination can be corrected either using calibration targets or estimating the illumination pattern from a series of images by fitting polynomial surfaces BIB002 . Another approach is to match the histograms of the images. Software that corrects for spectral and spatial illumination variations is becoming a standard package provided by most bright field manufacturers. This is an essential step for algorithms that heavily depend on color space computations. Yang and Foran BIB001 presented a robust color-based segmentation algorithm for histological structures that used image gradients estimated in the LUV color space to deal with issues of stain variability. In the next section, we give detailed description of correcting another artifact, tissue autofluorescence, in fluorescent images.