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AssigneeBoard of Regents, The University of Texas System
InventorsSos S. Agaian, Clara M. Mosquera Lopez, Ali Almuntashri, Richard Metzler
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The invention provides systems and methods for detection, grading, scoring and tele-screening of cancerous lesions. A complete scheme for automated quantitative analysis and assessment of human and animal tissue images of several types of cancers is presented. Various aspects of the invention are directed to the detection, grading, prediction and staging of prostate cancer on serial sections/slides of prostate core images, or biopsy images. Accordingly, the invention includes a variety of sub-systems, which could be used separately or in conjunction to automatically grade cancerous regions. Each system utilizes a different approach with a different feature set. For instance, in the quantitative analysis, textural-based and morphology-based features may be extracted at image- and (or) object-levels from regions of interest. Additionally, the invention provides sub-systems and methods for accurate detection and mapping of disease in whole slide digitized images by extracting new features through integration of one or more of the above-mentioned classification systems. The invention also addresses the modeling, qualitative analysis and assessment of 3-D histopathology images which assist pathologists in visualization, evaluation and diagnosis of diseased tissue. Moreover, the invention includes systems and methods for the development of a tele-screening system in which the proposed computer-aided diagnosis (CAD) systems. In some embodiments, novel methods for image analysis (including edge detection, color mapping characterization and others) are provided for use prior to feature extraction in the proposed CAD systems.
A method, performed by a processing unit, for computing pixels along object edges and producing a deinterlaced image from an interlaced source, the method comprising:
A method for computing the fractal dimension of color images comprising:
A method for carcinomas color region mapping comprising:
A textural-based system and methods for automatically detecting, classifying, and grading cancerous regions of a histology image comprising:
The systems as claimed in 13, wherein the mapping from 2-D image to 3-D images includes algorithms for 3-D reconstruction from a single 2-D image or from several 2-D slides. The processed images nay be gray scaled or colored images.
A method to obtain a multidimensional Gleason grading wherein a revised Gleason value is generated as follows:
A system for cancer prognosis, wherein the pathology report including all the outputs of systems claimed in 7, 9, 11 and 13 is integrated with patient information and PSA analysis results to construct feature vectors. The resulting feature vectors are used to aid pathologists in cancer prognosis, risk assessment and treatment planning.
A system and methods for cancer tele-screening and diagnosis based on biopsy image and an adjudication scheme, wherein expert pathologists and CAD systems are integrated.
A method and system constructing an optimal signal detector using a learning algorithm in tandem with a prediction algorithm (which may be one in the same) and data compression algorithm.
A system and method for determining the most likely distribution of Gleason patterns within a query image, comprising:
A system for cancer prognosis, wherein the pathology report including all the outputs of systems claimed in 30 through 41 is integrated with patient information and PSA analysis results to construct feature vectors. The resulting feature vectors are used to aid pathologists in cancer prognosis, risk assessment and treatment planning.
Dependencies are inferred from the claim text.