Methods and Software for Screening and Diagnosing Skin Lesions and Plant Diseases

a skin lesions and plant disease technology, applied in the field of dermoscopy and software, can solve the problems of deficient image sampling, processing and texture analysis methods, algorithms and plug-in features in the early stages of the ar

Inactive Publication Date: 2014-02-06
UNIV HOUSTON SYST
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

[0038]FIGS. 21A-21B illustrate the effect of sampling strategy on classification performan

Problems solved by technology

More specifically, the prior art is deficient in image sampling, processing and texture analysis methods, algorithms and plug-in feature

Method used

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  • Methods and Software for Screening and Diagnosing Skin Lesions and Plant Diseases
  • Methods and Software for Screening and Diagnosing Skin Lesions and Plant Diseases
  • Methods and Software for Screening and Diagnosing Skin Lesions and Plant Diseases

Examples

Experimental program
Comparison scheme
Effect test

example 1

Methods

[0073]An image, such as skin lesion, can be acquired using a smartphone camera, with or without an external attachment that can provide illumination and magnification, or can be loaded from the photo library to provide the diagnosis in real time. The application can process images taken either with the iPhone® camera or taken with an external camera and uploaded to the image library on the phone. To test the application, images from a large commercial library of skin cancer images that were annotated by dermatologists (7) are uploaded to the phone. Intra-observer and inter-observer agreement could be low for certain criteria (8). All images were segmented manually to provide an evaluative level against which the automated techniques of the applications presented herein were compared. Prior to processing, if necessary, the images are converted from color to 256 level greyscale.

[0074]The iPhone 4® is a smartphone developed by Apple Inc., and features an Apple® A4 ARM® processor...

example 2

Non-Uniform Sampling for Bag-of-Features Classification

Dataset

[0078]A dataset with 645 epiluminescence microscopy (ELM) images, in which 491 lesions are benign and 154 lesions are melanoma, was used. The images are mainly collected from a commercial database (7). The total image size ranges from 712×454 to 1,024×768 pixels, while the lesion size ranges from 7,662 to 804,527 pixels.

Procedure

[0079]For bag-of-features-based classification, first, patches are extracted from every lesion, and then, for each patch, patch descriptors are generated using color moments and Haar wavelet coefficients, which capture the color and texture information. Then, each patch is assigned to a codeword from a pre-learned codebook, using hard assignment, as described herein. After that, a final feature vector is generated by pooling the assignments of all patches extracted from the lesion. For the classifier, support vector machines (SVMs) with a χ2 kernel are used, which currently represent state-of-art ...

example 3

Evaluation of Sampling Strategies of Dermoscopic Interest Points (DIPs) in Melanomas Dataset

[0094]A dataset with 1,505 epiluminescence microscopy (ELM) images, in which 1,098 lesions are benign and 407 lesions are melanoma were used. The image size ranges from 712×454 to 1,024×768 pixels, and lesion size ranges from 7,662 to 804,527 pixels. Manual segmentation of all lesions is used to ensure that evaluation of the various sampling strategies is not affected by possible differences in automated identification of the lesion boundary.

Procedure

[0095]The lesion classification procedure (9) consists of five main steps: image sampling, feature extraction, coding, pooling, and final classification (10). For a given image, identify DIPs inside the lesion are identified first and then a patch is extracted around each DIP. On each patch, several low level texture and color features were computed using Haar wavelets and color moments, which are important for melanoma detection. In the coding s...

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Abstract

Provided herein are portable imaging systems, for example, a digital processor-implemented system for the identification and/or classification of an object of interest on a body, such as a human or plant body. The systems comprise a hand-held imaging device, such as a smart device, and a library of algorithms or modules that can be implemented thereon to process the imaged object, extract representative features therefrom and classify the object based on the representative features. Also provided are methods for the identifying or classifying an object of interest on a body that utilize the algorithms and an automated portable system configured to implement the same.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This non-provisional application claims benefit of priority under 35 U.S.C. §119(e) of provisional application U.S. Ser. No. 61 / 616,633, filed Mar. 28, 2012, now abandoned, the entirety of which is hereby incorporated by reference.FEDERAL FUNDING LEGEND[0002]This invention was made with governmental support under Grant Number IR21AR057921 awarded by the National Institutes of Health. The government has certain rights in the invention.BACKGROUND OF THE INVENTION[0003]1. Field of the Invention[0004]The present invention relates to the fields of dermoscopy and software for the practice thereof on handheld biomedical screening devices. Specifically, the present invention provides a library of image processing and texture analysis algorithms that run on embedded devices for screening lesions and for plant diseases.[0005]2. Description of the Related Art[0006]The American Cancer Society predicts that there will be approximately 68,130 new cases...

Claims

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/0012A61B5/0077A61B5/444G06T2207/10024G06T2207/20081G06T2207/30088G06V2201/032G06V2201/03
Inventor ZOURIDAKIS, GEORGE
Owner UNIV HOUSTON SYST
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