Mobile Architecture Using Cloud for Hashimoto's Thyroiditis Disease Classification

• US 20120220875A1
• Filed: 05/07/2012
• Published: 08/30/2012
• Est. Priority Date: 04/20/2010
• Status: Abandoned Application

• Current Assignee: Jasjit S. Suri
• Original Assignee: Jasjit S. Suri
• Inventors: Suri, Jasjit S.
• Application Number:
  US13/465,091
• Publication Number:
  US 20120220875A1
• Time in Patent Office: Days
• US Class Current: 600/443
• 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 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 70/60 relating to pathologies

Abstract

Hashimoto'"'"'s Thyroiditis (HT) is the most common type of inflammation of the thyroid gland and accurate diagnosis of HT would be advantageous in predicting thyroid failure. The application presents three tier architecture for image-based diagnosis and monitoring application using Cloud is described. The presentation layer is run on the tablet (mobile device), while the business and persistence layer runs on a single cloud or distributed on different Clouds in a multi-tenancy and multi-user application. Such architecture is used for automated data mining application for diagnosis of Hashimoto'"'"'s Thyroiditis (HT) Disease using ultrasound.

Claims

1. A computer-implemented method comprising:

receiving image data on a mobile presentation device, such as hand-held device having a display screen, from a current image of a patient record stored in a network cloud;

using a data processor in data communication with a business layer (tier 2) containing a data mining application in the cloud;

using the data processor in data communication with the business layer (tier 2) containing an automated data mining application in the cloud with several configurations for creating multiple business layers or fusion of business layers;

using the data processor in data communication with a persistence layer (tier 3) containing an automated data mining application in network communication with the business layer;

displaying the processed results on the presentation layer computed by the automated data mining application and computed using a combination of business layer and a persistence layer;

using the data processor in data communication with a presentation layer (tier-1) displaying the processed results computed by the automated data mining application and computed using a combination of business (tier-2) layer and a persistence layers (tier-3), and able to communicate between presentation layer, business layer and persistence layer of the three tier architecture;

using the combination of three tiers, where the business layer in combination of persistence layers uses a combination of training classifier and testing classifiers; and

using a testing classifier as an online system for computing a binary diagnostic index for Hashimoto'"'"'s Thyroiditis (HT) Disease.

2. The method as claimed in claim 1 which can be used for diagnosis or monitoring of Hashimoto'"'"'s Thyroiditis (HT) Disease.

3. The method as claimed in claim 1 which can be used for diagnosis or monitoring of benign vs. malignant Thyroid cancer index (ThyroScan™

).

4. The method as claimed in claim 1 where the Business layer (tier 2) can be an ultrasound B-mode data or an RF mode ultrasound data set for Hashimoto'"'"'s Thyroiditis HT diagnosis.

5. The method as claimed in claim 1 where the Business layer (tier 2) consists of online processor for computing of four grayscale features:

Entropy-based feature, Gabor Wavelet-based Feature, Inverse Moment-based feature, and Higher Order Spectra Features.

6. The method as claimed in claim 1 where the Business layer (tier 2) consists of online processor for computing the online features such as:

Entropy-based feature, Gabor Wavelet-based Feature, Inverse Moment-based feature, and Higher Order Spectra Features and further using a feature selector for selecting the best combination of features.

7. The method as claimed in claim 1 where the Business layer (tier 2) consists of online processor for computing the online features such as:

Entropy-based feature, Gabor Wavelet-based Feature, Inverse Moment-based feature, and Higher Order Spectra Features and further using a feature selector for selecting the best combination of features and further using these features in combination of off-line Thyroid vectors for diagnosis HT.

8. The method as claimed in claim 1 where the set-up of business layer (tier-2) can have several configurations controlled by the presentation layer (tier 1)—

such configurations can be using different classifiers such as SVM, KNN, BPPNN for HT diagnosis.

9. The method as claimed in claim 7 where the off-line Thyroid vectors uses the same set of four features:

Entropy-based feature, Gabor Wavelet-based Feature, Inverse Moment-based feature, and Higher Order Spectra Features.

10. The method as claimed in claim 1 where the Business layer (tier 3) can receive the MR data.

11. The method as claimed in claim 1 where the Business layer (tier 3) can be a CT data.

12. The method as claimed in claim 1 where the set-up of presentation layer (tier-1) is a hand-held device, a laptop or notebook or a desktop or an iPhone or a tablet and receives data from Business Layer and Persistence Layers using the controls of Presentation Layer.

13. The method as claimed in claim 1 where the set-up of business layer (tier-2) can be in one cloud and persistence layer (tier-3) can be in same or another cloud, so called distributed cloud architecture by splitting the different tiers of the architecture for computing a diagnostic index for benign vs. malignant tissue for thyroid cancer diagnosis, and diagnosis of HT.

14. The method as claimed in claim 1 where the set-up uses a wireless system for data transfer between the presentation layer and business layer and vice-versa.

15. The method as claimed in claim 1 where the set-up uses a wireless system for data transfer between the presentation layer and persistence layers and vice-versa.

16. The method as claimed in claim 6 where the business layer can be utilize any 2D or 3D segmentation engine for computation of region of interest (ROI) and then computing the grayscale features such as Entropy-based feature, Gabor Wavelet-based. Feature, Inverse Moment-based feature, and Higher Order Spectra Features in this region of interest.

17. The method as claimed in claim 16 where the business layer can be utilize any 2D or 3D segmentation engine for computation of region of interest (ROI), where the region of interest can be computed automatically or semi-automatically.

18. The method as claimed in claim 16 where the business layer can be utilize any 2D or 3D segmentation engine for computation of region of interest (ROI), where the region of interest can be computed using a trained atlas.

19. The method as claimed in claim 16 where the business layer can be utilize any 2D or 3D segmentation engine for computation of region of interest (ROI) and then computing the grayscale features such as Entropy-based feature, Gabor Wavelet-based Feature, Inverse Moment-based feature, and Higher Order Spectra Features in this region of interest and followed by feature selection system for selecting the best features.

20. The method as claimed in claim 1 where the business layer can be utilize thyroid image data from the left lobe or right lobe or can be combined using left and right lobe.