Combining information of different levels for content-based retrieval of digital pathology images
First Claim
Patent Images
1. A method of extracting a feature of an image programmed in a memory of a device comprising:
- a. receiving a pathology image;
b. performing a plurality of modes of quantization on the pathology image, wherein the plurality of modes of quantization include color quantization, texture quantization and diagnostic quantization, wherein the diagnostic quantization is implemented by assigning a label indicating a most probable cancer stage to each pixel in the pathology image using an automatic cancer grading analysis system;
c. extracting features from quantization maps of the pathology image, wherein each quantization map of the quantization maps is an integer array of a size equal to the pathology image; and
d. generating a feature vector of the pathology image, wherein the feature vector comprises multiple co-occurrence feature vectors, deduced from co-occurrence matrices computed at multiple scales, wherein an offset distance parameter is used to determine a scale at which pixel correlation is analyzed.
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Abstract
Content-based retrieval of digital pathology images (DPI) is a fundamental component in an intelligent DPI processing and management system. One key issue of content-based DPI retrieval is how to represent an image as a feature vector, capturing its key information that is most relevant to the goal of retrieval. A unified framework of extracting information of different levels for DPI, namely low level color information, middle level texture information and high level diagnostic information is described herein. Such information from all the levels are integrated to the end of content-based DPI retrieval.
18 Citations
24 Claims
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1. A method of extracting a feature of an image programmed in a memory of a device comprising:
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a. receiving a pathology image; b. performing a plurality of modes of quantization on the pathology image, wherein the plurality of modes of quantization include color quantization, texture quantization and diagnostic quantization, wherein the diagnostic quantization is implemented by assigning a label indicating a most probable cancer stage to each pixel in the pathology image using an automatic cancer grading analysis system; c. extracting features from quantization maps of the pathology image, wherein each quantization map of the quantization maps is an integer array of a size equal to the pathology image; and d. generating a feature vector of the pathology image, wherein the feature vector comprises multiple co-occurrence feature vectors, deduced from co-occurrence matrices computed at multiple scales, wherein an offset distance parameter is used to determine a scale at which pixel correlation is analyzed. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A system comprising:
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a. a non-transitory memory for storing an application, the application comprising; i. a mode module configured for performing a plurality of modes of quantization on a pathology image, wherein the plurality of modes of quantization include color quantization, texture quantization and diagnostic quantization, wherein the diagnostic quantization is implemented by assigning a label indicating a most probable cancer stage to each pixel in the pathology image using an automatic cancer grading analysis system; ii. an extraction module configured for extracting features from quantization maps of the pathology image, wherein each quantization map of the quantization maps is an integer array of a size equal to the pathology image; and iii. a generation module configured for generating a feature vector of the pathology image, wherein the feature vector comprises multiple co-occurrence feature vectors, deduced from co-occurrence matrices computed at multiple scales, wherein an offset distance parameter is used to determine a scale at which pixel correlation is analyzed; and b. a processor coupled to the memory, the processor configured for processing the application. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. An apparatus comprising:
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b. a non-transitory memory for storing an application, the application for; i. receiving a pathology image; ii. performing a plurality of modes of quantization on the pathology image, wherein the plurality of modes of quantization include color quantization, texture quantization and diagnostic quantization, wherein the diagnostic quantization is implemented by assigning a label indicating a most probable cancer stage to each pixel in the pathology image using an automatic cancer grading analysis system; iii. extracting features from quantization maps of the pathology image, wherein each quantization map of the quantization maps is an integer array of a size equal to the pathology image; and iv. generating a feature vector of the pathology image, wherein the feature vector comprises multiple co-occurrence feature vectors, deduced from co-occurrence matrices computed at multiple scales, wherein an offset distance parameter is used to determine a scale at which pixel correlation is analyzed; and c. a processor coupled to the memory, the processor configured for processing the application. - View Dependent Claims (18, 19, 20, 21, 22, 23, 24)
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Specification