Abstract: Methods, apparatus, and other embodiments associated with classifying a region of cancerous tissue using a Histogram of Hosoya are described. One example apparatus includes a set of logics that acquires an image of a region of tissue demonstrating cancerous pathology, constructs a cell graph of the region of tissue, decomposes the cell graph into a set of subgraphs, computes a Hosoya Index for a subgraph, constructs a Histogram of Hosoya for the image based on the distribution of the subgraphs, and classifies the image based on the Histogram of Hosoya. Embodiments of example apparatus may generate and display the Histogram of Hosoya for the image. A prognosis for the patient may be provided based on the distribution of the histogram.

Abstract: A system is provided for standardizing digital histological images so that the color space for a histological image correlates with the color space of a template image of the histological image. The image data for the image is segmented into a plurality of subsets that correspond to different tissue classes in the image. The image data for each subset is then compared with a corresponding subset in the template image. Based on the comparison, the color channels for the histological image subsets are shifted to create a series of standardized subsets, which are then combined to create a standardized image.

Abstract: The described invention provides a system and method for predicting disease outcome using a multi-field-of-view scheme based on image-based features from multi-parametric heterogenous images.

Abstract: A system and method for predicting disease outcome by analyzing a large, heterogeneous image by a boosted, multi-field-of-view (FOV) framework, based on image-based features from multi-parametric heterogeneous images, comprises (a) inputting the heterogeneous image; (b) generating a plurality of FOVs at a plurality of fixed FOV sizes, the method for generating the plurality of FOVs at a plurality of fixed FOV sizes comprising dividing simultaneously, via the computing device, the large, heterogeneous image into (i) a plurality of FOVs at a first fixed FOV size from among the plurality of fixed FOV sizes; and (ii) a plurality of FOVs at a second fixed FOV size from among the plurality of fixed FOV sizes; (c) producing simultaneously for the heterogeneous image a combined class decision for: (i) the plurality of FOVs at the first fixed FOV size, and (ii) the plurality of FOV s at the second fixed FOV size.

Abstract: Methods, apparatus, and other embodiments associated with classifying a region of cancerous tissue using a Histogram of Hosoya are described. One example apparatus includes a set of logics that acquires an image of a region of tissue demonstrating cancerous pathology, constructs a cell graph of the region of tissue, decomposes the cell graph into a set of subgraphs, computes a Hosoya Index for a subgraph, constructs a Histogram of Hosoya for the image based on the distribution of the subgraphs, and classifies the image based on the Histogram of Hosoya. Embodiments of example apparatus may generate and display the Histogram of Hosoya for the image. A prognosis for the patient may be provided based on the distribution of the histogram.

Abstract: The present invention relates to an image-based computer-aided prognosis (CAP) system and method that seeks to replicate the prognostic power of molecular assays in histopathology and pathological processes, including but not limited to cancer. Using only a tissue slide samples, a mechanism for digital slide scanning, and a computer, the present invention relates to an image-based CAP system and method which aims to overcome many of the drawbacks associated with prognostic molecular assays (e.g. Oncotype DX) including the high cost associated with the assay, limited laboratory facilities with specialized equipment, and length of time between biopsy and prognostic prediction.

Abstract: The described invention provides a system and method for predicting disease outcome using a multi-field-of-view scheme based on image-based features from multi-parametric heterogenous images.

Abstract: The present invention relates to a method and system for detecting biologically relevant structures in a hierarchical fashion, beginning at a low-resolution and proceeding to higher levels of resolution. The present invention also provides probabilistic pairwise Markov models (PPMMs) to classify these relevant structures. The invention is directed to a novel classification approach which weighs the importance of these structures. The present invention also provides a fast, efficient computer-aided detection/diagnosis (CAD) system capable of rapidly processing medical images (i.e. high throughput). The computer-aided detection/diagnosis (CAD) system of the present invention allows for rapid analysis of medical images the improving the ability to effectively detect, diagnose, and treat certain diseases.

Abstract: A system and method for predicting disease outcome by analyzing a large, heterogeneous image by a boosted, multi-field-of-view (FOV) framework, based on image-based features from multi-parametric heterogeneous images, comprises (a) inputting the heterogeneous image; (b) generating a plurality of FOVs at a plurality of fixed FOV sizes, the method for generating the plurality of FOVs at a plurality of fixed FOV sizes comprising dividing simultaneously, via the computing device, the large, heterogeneous image into (i) a plurality of FOVs at a first fixed FOV size from among the plurality of fixed FOV sizes; and (ii) a plurality of FOVs at a second fixed FOV size from among the plurality of fixed FOV sizes; (c) producing simultaneously for the heterogeneous image a combined class decision for: (i) the plurality of FOVs at the first fixed FOV size, and (ii) the plurality of FOV s at the second fixed FOV size.

Abstract: The present invention relates to an image-based computer-aided prognosis (CAP) system and method that seeks to replicate the prognostic power of molecular assays in histopathology and pathological processes, including but not limited to cancer. Using only a tissue slide samples, a mechanism for digital slide scanning, and a computer, the present invention relates to an image-based CAP system and method which aims to overcome many of the drawbacks associated with prognostic molecular assays (e.g. Oncotype DX) including the high cost associated with the assay, limited laboratory facilities with specialized equipment, and length of time between biopsy and prognostic prediction.

Abstract: The present invention relates to a method and system for detecting biologically relevant structures in a hierarchical fashion, beginning at a low-resolution and proceeding to higher levels of resolution. The present invention also provides probabilistic pairwise Markov models (PPMMs) to classify these relevant structures. The invention is directed to a novel classification approach which weighs the importance of these structures. The present invention also provides a fast, efficient computer-aided detection/diagnosis (CAD) system capable of rapidly processing medical images (i.e. high throughput). The computer-aided detection/diagnosis (CAD) system of the present invention allows for rapid analysis of medical images the improving the ability to effectively detect, diagnose, and treat certain diseases.