Systems and methods for automated screening and prognosis of cancer from whole-slide biopsy images

• US 10,192,099 B2
• Filed: 07/20/2016
• Issued: 01/29/2019
• Est. Priority Date: 09/27/2011
• Status: Active Grant

• Current Assignee: Board of Regents of the University of Texas System (University of Texas System)
• Original Assignee: Board of Regents of the University of Texas System (University of Texas System)
• Inventors: Agaian, Sos S., Lopez, Clara M. Mosquera, Almuntashri, Ali, Metzler, Richard
• Primary Examiner(s): Shah, Utpal D
• Application Number:
  US15/215,157
• Publication Number:
  US 20170169276A1
• Time in Patent Office: 923 Days
• CPC Class Codes: A61B 10/0241 for prostate
• CPC Class Codes: A61B 5/4381 Prostate evaluation or disorder diagnosis
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G01N 15/1433 using image recognition
• CPC Class Codes: G06F 18/211 Selection of the most significant subset of features
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/10056 Microscopic image
• CPC Class Codes: G06T 2207/20036 Morphological image processing
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/30024 Cell structures in vitro; Tissue sections in vitro
• CPC Class Codes: G06T 2207/30081 Prostate
• CPC Class Codes: G06T 2207/30096 Tumor; Lesion
• CPC Class Codes: G06T 5/20 using local operators
• CPC Class Codes: G06T 5/70 Denoising; Smoothing
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/13 Edge detection
• CPC Class Codes: G06T 7/181 : involving edge growing; involving edge linking
• CPC Class Codes: G06T 7/90 : Determination of colour characteristics
• CPC Class Codes: G06V 20/69 : Microscopic objects, e.g. biological cells or cellular parts
• CPC Class Codes: G06V 20/695 : Preprocessing, e.g. image segmentation
• CPC Class Codes: G06V 20/698 : Matching; Classification
• CPC Class Codes: G16H 30/20 : for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients

Abstract

Systems and methods for detection, grading, scoring and tele-screening of cancerous lesions are described. 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.

Claims

1. A method for automatically detecting classifying, and grading cancerous regions of one or more biopsy images comprising:

performing an image color standardization procedure on the one or more biopsy images to produce one or more color standardized biopsy images;

performing edge detection on the one or more color standardized biopsy images;

generating sets of texture-based feature vectors from the one or more color standardized biopsy images, wherein generating sets of texture-based feature vectors comprises extraction of a set of features from Fourier and wavelet transforms and fractal analysis of the one or more color standardized biopsy images;

training a classifier by using the generated sets of texture-based feature vectors;

classifying the one or more biopsy images according to the Gleason grading system;

using the result of the classification to determine the Gleason score of the one or more biopsy images;

wherein fractal analysis of the one or more color standardized biopsy images comprises;

performing image filtering on the one or more color standardized biopsy images depending on nature of noise in the one or more color standardized biopsy image;

binarizing the one or more color standardized biopsy images to produce binary images of the one or more color standardized biopsy images,

calculating fractal dimension of the binary images by using different grid sizes based on a Differential Box Counting (DBC) algorithm; and

fusing resulting fractal dimensions; and

wherein binarizing the one or more color standardized biopsy images comprises;

performing image filtering on the one or more color standardized biopsy images to produce one or more filtered images;

smoothing the one or more filtered images using shape-dependent filters;

calculating gradient vectors in the one or more filtered images using different kernels;

selecting an edge angle in the one or more filtered images;

determining threshold values within a local dynamic range in the one or more filtered images,

generating several edge maps in the one or more filtered images, and

fusing the generated edge maps together to form one or more binary images of the one or more color standardized biopsy images.

2. The method of claim 1, wherein the cross-validation methods are selected from a set of algorithms comprising:

5-fold, 10-fold, hold-out or leave-one-out.

3. The method of claim 1, further comprising applying an algorithm for image edge-preserving contrast enhancement which is based on HVS, Parameterized Logarithm Image Processing operations, and is integrated with morphological log-ratio approach in order to come up with an effective edge detection operator that is sensitive to edges of dark areas in the image.