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Automated processing of digital histopathology slides has the potential to streamline patient care and provide new tools for cancer classification and grading. Before automatic analysis is possible, quality control procedures are applied to ensure that each image can be read consistently. One important quality control step is color normalization of the slide image, which adjusts for color variances...
Pattern recognition in tissue biopsy images can assist in clinical diagnosis and identify relevant image characteristics linked with various biological characteristics. Although previous work suggests several informative imaging features for pattern recognition, there exists a semantic gap between characteristics of these features and pathologists' interpretation of histopathological images. To address...
The emergence of large multi-platform and multi-scale data repositories in biomedicine has enabled the exploration of data integration for holistic decision making. In this research, we investigate multi-modal genomic, proteomic, and histopathological image data integration for prediction of ovarian cancer clinical endpoints in The Cancer Genome Atlas (TCGA). Specifically, we study two data integration...
We present a color segmentation approach based on a two-dimensional color map derived from the input image. Pathologists stain tissue biopsies with various colored dyes to see the expression of biomarkers. In these images, because of color variation due to inconsistencies in experimental procedures and lighting conditions, the segmentation used to analyze biological features is usually ad-hoc. Many...
We propose an automatic color segmentation system that (1) incorporates domain knowledge to guide histological image segmentation and (2) normalizes images to reduce sensitivity to batch effects. Color segmentation is an important, yet difficult, component of image-based diagnostic systems. User-interactive guidance by domain experts-i.e., pathologists-often leads to the best color segmentation or...
Aiming at question that low identification precision of time series model system in noise, the ARMA parameters are estimated using a damped sinusoidal model representation of the autocorrelation function of the noise ARMA signal. The AR parameters are obtained directly form the estimates of the damped sinusoidal model parameters with guaranteed stability. The MA parameters are estimated using a correlation...
Color variation in medical images degrades the classification performance of computer aided diagnosis systems. Traditionally, color segmentation algorithms mitigate this variability and improve performance. However, consistent and robust segmentation remains an open research problem. In this study, we avoid the tenuous phase of color segmentation by adapting a bag-of-features approach using scale...
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