Biomedical subjects

Dmitry B Goldgof

Publications and source records attributed to Dmitry B Goldgof.

4 recordsLinked to original sources

PubMed2004

Selection of patients for clinical trials: an interactive web-based system.

The purpose of a clinical trial is to evaluate a new treatment procedure. When medical researchers conduct a trial, they recruit participants with appropriate health problems and medical histories. To select participants, they analyze medical records of the available patients, which has traditionally been a manual procedure. We describe an expert system that helps to select patients for clinical trials. If the available data are insufficient for choosing patients, the system suggests additional medical tests and finds an ordering of the tests that reduces their total cost. Experiments show that the system can increase the number of selected patients. We also present an interface that enables a medical researcher to add clinical trials and selection criteria without the help of a programmer. The addition of a new trial takes 10-20 min, and novice users learn the functionality of the interface in about an hour.

Breast Neoplasms↗

PubMed2004

A modeling approach for burn scar assessment using natural features and elastic property.

A modeling approach is presented for quantitative burn scar assessment. Emphases are given to: 1) constructing a finite-element model from natural image features with an adaptive mesh and 2) quantifying the Young's modulus of scars using the finite-element model and regularization method. A set of natural point features is extracted from the images of burn patients. A Delaunay triangle mesh is then generated that adapts to the point features. A three-dimensional finite-element model is built on top of the mesh with the aid of range images providing the depth information. The Young's modulus of scars is quantified with a simplified regularization functional, assuming that the knowledge of the scar's geometry is available. The consistency between the relative elasticity index and the physician's rating based on the Vancouver scale (a relative scale used to rate burn scars) indicates that the proposed modeling approach has high potential for image-based quantitative burn scar assessment.

Algorithms↗

PubMed2004

Recognizing plankton images from the shadow image particle profiling evaluation recorder.

We present a system to recognize underwater plankton images from the shadow image particle profiling evaluation recorder (SIPPER). The challenge of the SIPPER image set is that many images do not have clear contours. To address that, shape features that do not heavily depend on contour information were developed. A soft margin support vector machine (SVM) was used as the classifier. We developed a way to assign probability after multiclass SVM classification. Our approach achieved approximately 90% accuracy on a collection of plankton images. On another larger image set containing manually unidentifiable particles, it also provided 75.6% overall accuracy. The proposed approach was statistically significantly more accurate on the two data sets than a C4.5 decision tree and a cascade correlation neural network. The single SVM significantly outperformed ensembles of decision trees created by bagging and random forests on the smaller data set and was slightly better on the other data set. The 15-feature subset produced by our feature selection approach provided slightly better accuracy than using all 29 features. Our probability model gave us a reasonable rejection curve on the larger data set.

Algorithms↗

PubMed2004

A methodology for extracting objective color from images.

We present a methodology for correcting color images taken in practical indoor environments, such as laboratories, factories, and studios, that explicitly models illuminant location, surface reflectance and geometry, and camera responsivity. We explicitly model surfaces by taking our color images with corresponding registered three-dimensional (3-D) range images, which provide surface orientation and location information for every point in the scene. We automatically detect regions where color correction should not be applied, such as specularities, coarse texture regions, and jump edges. This correction results in objective color measures of the imaged surfaces. This kind of integrated, comprehensive system of color correction has not existed until now. i.e., it is the first of its kind in computer vision. We demonstrate results of applying this methodology to real images for applications in photorealistic rerendering, skin lesion detection, burn scar color measurement, and general color image enhancement. We also have tested the method under different lighting configurations and with three different range scanners.

Algorithms↗