METHODS AND ARRANGEMENTS FOR IDENTIFYING DERMATOLOGICAL DIAGNOSES WITH CLINICALLY NEGLIGIBLE PROBABILTIES

• US 20150003699A1
• Filed: 05/28/2014
• Published: 01/01/2015
• Est. Priority Date: 04/18/2013
• Status: Active Grant

• Current Assignee: Digimarc Corporation
• Original Assignee: Digimarc Corporation
• Inventors: Davis, Bruce L., Rodriguez, Tony F., Reed, Alastair M., Stach, John, Rhoads, Geoffrey B., Conwell, William Y., Thagadur Shivappa, Shankar, Sharma, Ravi K., Gibson, Richard F.
• Granted Patent: US 9,504,420 B2
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: A61B 2576/02 specially adapted for a particular organ or body part
• CPC Class Codes: A61B 5/0075 by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy A61B5/0071 takes precedence
• CPC Class Codes: A61B 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/1032 Determining colour for diagnostic purposes measuring colour in general G01J3/46
• CPC Class Codes: A61B 5/1034 by means of colour cards
• CPC Class Codes: A61B 5/441 Skin evaluation, e.g. for skin disorder diagnosis
• CPC Class Codes: A61B 5/444 Evaluating skin marks, e.g. mole, nevi, tumour, scar
• CPC Class Codes: A61B 5/445 Evaluating skin irritation or skin trauma, e.g. rash, eczema, wound, bed sore
• CPC Class Codes: A61B 5/4806 Sleep evaluation A61B5/4821 takes precedence; devices for inducing sleep A61M21/02
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/7425 Displaying combinations of multiple images regardless of image source, e.g. displaying a reference anatomical image with a live image geometric transforms for image registration G06T3/14; enhancement using image fusion G06T5/50; algorithms for registration of images G06T7/30
• CPC Class Codes: A61B 5/743 Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
• CPC Class Codes: A61B 5/7485 Automatic selection of region of interest
• CPC Class Codes: G06F 16/245 Query processing
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06T 2207/30088 Skin; Dermal
• CPC Class Codes: G06T 5/40 using histogram techniques
• CPC Class Codes: G06T 7/0012 : Biomedical image inspection
• CPC Class Codes: G10L 19/018 : Audio watermarking, i.e. embedding inaudible data in the audio signal
• 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 40/67 : for remote operation
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16Z 99/00 : Subject matter not provided for in other main groups of this subclass
• CPC Class Codes: Y02A 50/30 : Against vector-borne diseases, e.g. mosquito-borne, fly-borne, tick-borne or waterborne diseases whose impact is exacerbated by climate change
• CPC Class Codes: Y02A 90/10 : Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

Reference imagery of dermatological conditions is compiled in a crowd-sourced database (contributed by clinicians and/or the lay public), together with associated diagnosis information. A user later submits a query image to the system (e.g., captured with a smartphone). Image-based derivatives for the query image are determined (e.g., color histograms, FFT-based metrics, etc.), and are compared against similar derivatives computed from the reference imagery. This comparison identifies diseases that are not consistent with the query image, and such information is reported to the user. Depending on the size of the database, and the specificity of the data, 90% or more of candidate conditions may be effectively ruled-out, possibly sparing the user from expensive and painful biopsy procedures, and granting some peace of mind (e.g., knowledge that an emerging pattern of small lesions on a forearm is probably not caused by shingles, bedbugs, malaria or AIDS). A great number of other features and arrangements are also detailed.

Claims

1. A method comprising:

receiving first imagery depicting a part of a mammalian body that evidences a symptom of a possible pathological condition;

processing the received imagery to derive one or more image parameter(s);

searching a data structure for reference information, based on the derived image parameter(s);

from the reference information, determining result information, said determining including identifying one or more particular pathological conditions that is inconsistent with the pathological condition evidenced by said depicted part of the body; and

communicating at least some of the result information to a user.

2. The method of claim 1 in which said communicating includes identifying to the user at least one particular pathological condition that is inconsistent with the pathological condition evidenced by said depicted part of the body.

3. The method of claim 1 in which the first imagery depicts said part of the body at a first time, and the method further includes:

receiving second imagery depicting said part of the body at a second time, later than the first time;

determining data about a change in said symptom between the first and second times, based on said first and second imagery; and

using said determined data in said identifying the one or more particular pathological conditions that is not the pathological condition evidenced by said depicted part of the body.

4. The method of claim 1 in which the processing comprises one or more of:

determining, or performing, a color histogram, a blob analysis, and a frequency domain transformation.

5. The method of claim 2 in which the reference information includes multiple sets of reference data, each corresponding to a reported diagnosis of a pathological condition in a reference subject, and each including reference image-based information.

6. The method of claim 5 that further includes identifying plural candidate pathological conditions consistent with said received first imagery, and presenting a listing of said conditions to the user, ranked by probability.

7. The method of claim 6 in which plural of said sets of reference data each includes drug profile data indicating one or more drug(s) taken by said respective reference subject, and the method includes:

identifying a drug most commonly associated with one of said candidate pathological conditions; and

reporting said identified drug to the user as a further clue to diagnosis.

8. The method of claim 1 in which the mammalian body is a non-human body.

9. The method of claim 1 in which the first imagery includes a known object distinct from the body, and the processing includes determining a camera pose relative to the body based on apparent geometrical distortion of the object in said imagery.

10-58. -58. (canceled)