Classification and Assessment of Hand Arthritis Stage using Support Vector Machine.

Arthritis is one of the most common health problems affecting people around the world. The goal of the work presented work is to classify and categorizing hand arthritis stages for patients, who may be developing or have developed hand arthritis, using machine learning. Stage classification was done using finger border detection, developed curvature analysis, principal components analysis, support vector machine and K-nearest neighbor algorithms.

Automatic detection and severity measurement of eczema using image processing.

Chronic skin diseases like eczema may lead to severe health and financial consequences for patients if not detected and controlled early. Early measurement of disease severity, combined with a recommendation for skin protection and use of appropriate medication can prevent the disease from worsening. Current diagnosis can be costly and time-consuming.

Automated image analysis of digital colposcopy for the detection of cervical neoplasia.

Digital colposcopy is a promising technology for the detection of cervical intraepithelial neoplasia. Automated analysis of colposcopic images could provide an inexpensive alternative to existing screening tools. Our goal is to develop a diagnostic tool that can automatically identify neoplastic tissue from digital images.

Methodology of a real-time quality control for the multispectral digital colposcope (MDC).

Background: The diagnostic ability of algorithms developed for the Multispectral Digital Colposcope (MDC) is highly dependent on the quality of the image. The field of objective medical image quality analysis has great potential but has not been well exploited. Various researchers have reported different measures of image quality but with an existence of a reference image.

Results of a pilot study of multispectral digital colposcopy for the in vivo detection of cervical intraepithelial neoplasia.

Objective: Fluorescence spectroscopy is a promising technology for the detection of cervical squamous intraepithelial precancers and cancers. To date, many investigators have focused on point spectroscopy as an adjunct to diagnostic colposcopy. A device that visualizes the whole field of the cervix is needed for screening.

Optical technologies for cervical neoplasia: update of an NCI program project grant.

Cervical cancer is the second most common cancer in women worldwide and the leading cause of cancer mortality in women in developing countries. In the United States, over $6 billion is spent annually in the evaluation and treatment of low-grade lesions, many of which do not develop into full-blown cancer. In developing countries, however, the chief concern is that cervical cancer goes undetected because of the cost of testing and the lack of resources and trained personnel to screen and diagnose the disease.

Multispectral digital microscopy for in vivo monitoring of oral neoplasia in the hamster cheek pouch model of carcinogenesis.

In this study we use a multi-spectral digital microscope (MDM) to measure multi-spectral auto-fluorescence and reflectance images of the hamster cheek pouch model of DMBA (dimethylbenz[alpha]anthracene)- induced oral carcinogenesis. The multi-spectral images are analyzed both in the RGB (red, green, blue) color space as well as in the YCbCr (luminance, chromatic minus blue, chromatic minus red) color space. Mean image intensity, standard deviation, skewness, and kurtosis are selected as features to design a classification algorithm to discriminate normal mucosa from neoplastic tissue.

Multispectral digital colposcopy for in vivo detection of cervical cancer.

We present a multispectral digital colposcope (MDC) to measure multispectral autofluorescence and reflectance images of the cervix by using an inexpensive color CCD camera. The diagnostic ability of the MDC was evaluated by application of MDC spectral response to fluorescence and reflectance spectra measured from a large clinical trial. High diagnostic performance was achieved by use of only two excitation wavelengths: 330 and 440 nm.