Abstract: A method and system for selecting a region of interest (ROI) in a tissue or groups of cells, in which one or more of the steps can be automated. In various exemplary embodiments, the method and system can be fully-automated. In various exemplary embodiments, the method and system can be applied to centrosome analysis with excellent accuracy and precision. Centrosome analysis is an important tool in very early cancer diagnosis and cancer prognosis.

Abstract: A method and system for selecting a region of interest (ROI) in a tissue or groups of cells, in which one or more of the steps can be automated. In various exemplary embodiments, the method and system can be fully-automated. In various exemplary embodiments, the method and system can be applied to centrosome analysis with excellent accuracy and precision. Centrosome analysis is an important tool in very early cancer diagnosis and cancer prognosis.

Abstract: The subject invention concerns methods for the detection, diagnosis, and/or prognosis of cancer by analyzing centrosomal features. In one embodiment, a method includes receiving an image of one or more cells; selecting a region of interest in one cell; segmenting the region of interest to delineate at least one centrosomal; extracting one or more features from the segmented image; and analyzing the extracted features to diagnose cancer. In another embodiment, the progression of cancer can be predicted through analysis and classification of the extracted features. In one embodiment, the method can be performed by a quantitative cancer analysis system including a diagnosis module and/or a prognosis module. In one embodiment, the method can be performed using an image processing system.

Abstract: The subject invention concerns methods for the detection, diagnosis, and/or prognosis of cancer by analyzing centrosomal features. In one embodiment, a method includes receiving an image of one or more cells; selecting a region of interest in one cell; segmenting the region of interest to delineate at least one centrosomal; extracting one or more features from the segmented image; and analyzing the extracted features to diagnose cancer. In another embodiment, the progression of cancer can be predicted through analysis and classification of the extracted features. In one embodiment, the method can be performed by a quantitative cancer analysis system including a diagnosis module and/or a prognosis module. In one embodiment, the method can be performed using an image processing system.

Abstract: The subject invention concerns methods for the detection, diagnosis, and/or prognosis of cancer by analyzing centrosomal features. In one embodiment, a method includes receiving an image of one or more cells; selecting a region of interest in one cell; segmenting the region of interest to delineate at least one centrosomal; extracting one or more features from the segmented image; and analyzing the extracted features to diagnose cancer. In another embodiment, the progression of cancer can be predicted through analysis and classification of the extracted features. In one embodiment, the method can be performed by a quantitative cancer analysis system including a diagnosis module and/or a prognosis module. In one embodiment, the method can be performed using an image processing system.

Abstract: The subject invention concerns methods for the detection, diagnosis, and/or prognosis of cancer by analyzing centrosomal features. In one embodiment, a method includes receiving an image of one or more cells; selecting a region of interest in one cell; segmenting the region of interest to delineate at least one centrosomal; extracting one or more features from the segmented image; and analyzing the extracted features to diagnose cancer. In another embodiment, the progression of cancer can be predicted through analysis and classification of the extracted features. In one embodiment, the method can be performed by a quantitative cancer analysis system including a diagnosis module and/or a prognosis module. In one embodiment, the method can be performed using an image processing system.

Abstract: The present invention is a method for early detection of cancer including the steps of identifying tissue from asymptomatic individuals, gathering a substantially homogenous cell population from the tissue by laser capture microdissection (LCM), analyzing the cell population with mass spectrometry and identifying predetermined peaks in the mass spectrometry associated with an overexpression of proteins in premalignant and malignant cells.

Abstract: The present invention is a computer-aided pathological diagnosis method for the classification of cancer cells in a tissue specimen based on a digital cellular image of the tissue specimen. The method of the present invention includes the steps of, extracting the histological characteristic features of the cellular image using preprocessing algorithms having adaptive strategies to enhance the cellular image, declustering the extracted histological characteristic features of the cellular image to isolate the individual cells and the nuclei inside the cells, segmenting the declustered cellular image, labeling the segmented cellular image and classifying the cells in the labeled cellular image as cancer cells or non-cancer cells.

Abstract: Luminescent semiconductor quantum dots (QDs) conjugated with biomolecules to serve as sensitive probes for early detection of the cancer cells, specifically for ovarian cancer and lung cancer, which represents the most lethal malignancies. The luminescence characterization of the bin-conjugated QDs with cancer specific antigens using linkage molecules. Photo-enhancement is measured at various laser density power, temperatures and laser wavelengths.

Abstract: The present invention is a computer-aided pathological diagnosis method for the classification of cancer cells in a tissue specimen based on a digital cellular image of the tissue specimen. The method of the present invention includes the steps of, extracting the histological characteristic features of the cellular image using preprocessing algorithms having adaptive strategies to enhance the cellular image, declustering the extracted histological characteristic features of the cellular image to isolate the individual cells and the nuclei inside the cells, segmenting the declustered cellular image, labeling the segmented cellular image and classifying the cells in the labeled cellular image as cancer cells or non-cancer cells.

Abstract: Luminescent semiconductor quantum dots (QDs) conjugated with biomolecules to serve as sensitive probes for early detection of the cancer cells, specifically for ovarian cancer and lung cancer, which represents the most lethal malignancies. The luminescence characterization of the bin-conjugated QDs with cancer specific antigens using linkage molecules. Photo-enhancement is measured at various laser density power, temperatures and laser wavelengths.