Uterine cervical cancer computer-aided-diagnosis (CAD)

• US 20090046905A1
• Filed: 02/03/2005
• Published: 02/19/2009
• Est. Priority Date: 02/03/2005
• Status: Active Grant

• Current Assignee: STI Medical Systems LLC
• Original Assignee: STI Medical Systems LLC
• Inventors: Lange, Holger, Wolters, Rolf Holger
• Granted Patent: US 7,664,300 B2
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: A61B 5/0059 using light, e.g. diagnosis by transillumination, diascopy, fluorescence A61B5/0093, A61B5/1455, A61B5/02416 and A61B5/0261 take precedence
• CPC Class Codes: A61B 5/053 Measuring electrical impedance or conductance of a portion of the body
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/30004 Biomedical image processing
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/30 Determination of transform parameters for the alignment of images, i.e. image registration
• CPC Class Codes: G16H 30/20 for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 30/40 for processing medical images, e.g. editing
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 70/20 relating to practices or guidelines

Abstract

Uterine cervical cancer Computer-Aided-Diagnosis (CAD) according to this invention consists of a core processing system that automatically analyses data acquired from the uterine cervix and provides tissue and patient diagnosis, as well as adequacy of the examination. The data can include, but is not limited to, color still images or video, reflectance and fluorescence multi-spectral or hyper-spectral imagery, coherent optical tomography imagery, and impedance measurements, taken with and without the use of contrast agents like 3-5% acetic acid, Lugol'"'"'s iodine, or 5-aminolevulinic acid. The core processing system is based on an open, modular, and feature-based architecture, designed for multi-data, multi-sensor, and multi-feature fusion. The core processing system can be embedded in different CAD system realizations. For example: A CAD system for cervical cancer screening could in a very simple version consist of a hand-held device that only acquires one digital RGB image of the uterine cervix after application of 3-5% acetic acid and provides automatically a patient diagnosis. A CAD system used as a colposcopy adjunct could provide all functions that are related to colposcopy and that can be provided by a computer, from automation of the clinical workflow to automated patient diagnosis and treatment recommendation.

Claims

1. A process for diagnosing cancer in a cervix, comprising:

spacially registering onto a single image of said cervix multiple calibrated data sets, each of said data sets being a different type of calibrated data which can be utilized for the detection of cervical cancer;

applying at least two selected diagnostic algorithms to at least one of said multiple data sets to extract colposcopic features from said data sets to obtain classifications of said extracted features;

combining classifications of said extracted features;

assessing examination adequacy of said combined classifications; and

if said examination adequacy is sufficient, determining tissue diagnosis;

whereby reliability of said tissue diagnosis is increased.

2. A process according to claim 1, wherein said different types of calibrated data include data selected from the group consisting of video or still images of fluorescence, reflectance, electrical impedance, or coherent optical tomography imagery.

3. A process according to claim 1, wherein said different types of calibrated data include data selected from the group consisting of multiple images taken over time, and multiple images taken from different examinations.

4. A process according to claim 1, wherein said diagnostic algorithms identify regions of colposcopic interest and are selected from the group consisting of acetowhite decay, vessel structure and lesion margins.