Histopathological Image Analysis: A Review <s> B. Global Scene Segmentation Approaches <s> Automated image analysis of bronchial tissue offers the opportunity to quantify stained area and staining intensity in a standardized way to obtain robust estimates of inflammatory cell counts and cytokine expression from multiple large areas of histopathologic sections. We compared fully automated digital image analysis with interactive digital cell counting and semiquantitative scoring of cytokine expression in terms of repeatability and agreement in bronchial biopsies in 52 patients with mild to moderate atopic asthma. Immunohistochemistry with antibodies against CD3, interleukin (IL)-4, IL-5, and interferon-gamma protein was performed on frozen tissue sections, using 3-amino-9-ethylcarbazole as chromogen and hematoxylin as counterstaining. IL-4 and IL-5 messenger RNAs were localized by in situ hybridization without hematoxylin staining. Separation of 3-amino-9-ethylcarbazole and hematoxylin-stained pixels was achieved by linear combination of red- and blue-filtered gray-scale images. Using baseline biopsy specimens, fully automated CD3+ cell counts showed perfect repeatability (r = 1.0) and a strong linear relationship with the interactive procedure (r = 0.98). Automated densitometry showed perfect repeatability (1.0) and a moderate to strong relationship with semiquantitative scoring of protein and messenger RNA expression (r = 0.43-0.89). Relationships between automated and semiquantitative assessments of changes in cytokine expression during 2 years of follow-up were moderate to strong (r = 0.40-0.84). We conclude that fully automated cell counts and automated densitometric analyses in bronchial tissue of patients with asthma are unbiased and help to reduce variability in inflammatory outcomes. <s> BIB001 </s> Histopathological Image Analysis: A Review <s> B. Global Scene Segmentation Approaches <s> Peripheral neuroblastic tumors (pNTs) make the most commonly encountered tumor groups in children. Neuroblastoma, one of the categories in pNTs, is known to have unique biological behaviors with variable clinical prognoses of the patients. Part of the neuroblastoma prognosis is closely related with grade of neuroblastic differentiation. In this work, we present an automatic classification system that includes a novel segmentation method using the EM algorithm with the Fisher-Rao criterion as its kernel. This is followed by a classification stage with classifiers applied to the actual neuroblastoma images. The good classification accuracy suggests that the developed method is promising in automating this pathological assessment <s> BIB002
In  , a unified segmentation algorithm for sub-cellular compartmentalization was presented. Quantitation of biomarkers at sub-cellular resolution requires segmentation of sub-cellular compartments such as nuclei, membranes, and cytoplasm. While different segmentation algorithms can be used for each of the sub-cellular compartments, an alternative is to use the same algorithm in different modes. The algorithm in  captured a set of bright pixels sharing a common shape distribution. The algorithm used a set of three features, one is the fluorescent emission intensity, and the other two are based on curvature descriptors that are computed from the eigenvalues of the Hessian matrix. For an image, , the eigenvalues ( ) of the Hessian matrix encode the curvature information of the image, and provide useful cues for detecting ridge-like membrane structures, or blob-like nuclei structures. However, the eigenvalues are dependent on image brightness. The following two curvature-based features are independent of image brightness: and referred to as shape index, and normalized-curvature index, respectively. This is essentially the same as defining the eigenvalues in a polar coordinate system. This transformation also results in bounded features, , and . The estimation process starts with the expected distributions of the shape index for the structures to be segmented. For example, for bright membrane and vessel like structures the shape index is close to , because the smaller eigenvalue is negative and the larger eigenvalue approaches to zero. On the other hand, for the blob-like nuclei structures, the shape index is close to , because both eigenvalues are negative and close in value. For both structures, positive values indicate a pixel being more like a background. These constraints are used to compute the initial foreground and background sets for membrane and nuclei structures. An initial segmentation based on the shape index and the normalized-curvature index separates the image pixels into three subsets: background, foreground, and indeterminate. The indeterminate subset comprises all the pixels that are not included in the background or foreground subsets. From these subsets, the background and foreground intensity distributions, as well as the intensity log-likelihood functions are estimated. The algorithm keeps iterating by using two out of the three features at a time to estimate the distribution of the feature that is left out. In the final step, these log-likelihood functions are combined to determine the overall likelihood function. A probability map that represents the probability of a pixel being a foreground is calculated. Cytoplasm can be detected either by using a specific cytoplasmic marker, or can be detected using computational methods using the fact that the cytoplasmic areas are between nuclear and membrane areas. For most cancer tissue types, it is very important to differentiate the epithelial tissue from the stromal and connective tissue, so that for IHC studies the expression levels of most markers in the epithelial regions can be quantified. Computational methods that use the high connectivity of membrane meshes can be used to differentiate the epithelial regions. For the sample images, any connected component larger than 800 pixels is accepted as a part of the epithelial mask. The nuclei set is then separated into epithelial nuclei and stromal nuclei using the epithelial mask. EMLDA is an image segmentation method, which uses the Fisher-Rao criterion as the kernel of the expectation maximization (EM) algorithm BIB002 . Typically, the EM-algorithm is used to estimate the parameters of some parameterized distributions, such as the popular Gaussian mixture models, and assign labels to data in an iterative way. Instead, the EMLDA algorithm uses the linear discriminant analysis (LDA), a supervised classification technique, as the kernel of EM-algorithm and iteratively group data points projected to a reduced dimensional feature space in such a way that the separability across all classes is maximized. In BIB001 , the authors successfully applied this approach in the context of histopathological image analysis to achieve the segmentation of digitized H&E stained whole-slide tissue samples.