Rutgers, the State University of New Jersey

University of Pennsylvania

Case Western Reserve University

Ajay Basavanhally

Anant Madabhushi

Michael D Feldman

James P Monaco

Sahirzeeshan Ali

John Tomaszewski

Shridar Ganesan

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.

Histogram of hosoya index (HoH) features for quantitative histomorphometry

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.

Integrated multivariate image-based method for disease outcome predicition

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.

Boosted consensus classifier for large images using fields of view of various sizes

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.

Image-based risk score&#x2014;A prognostic predictor of survival and outcome from digital histopathology

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.

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Skillman, NJ, United States of America

Ajay Basavanhally