Biomedical subjects

D B Goldgof

Publications and source records attributed to D B Goldgof.

5 recordsLinked to original sources

PubMed1998

Automatic tumor segmentation using knowledge-based techniques.

A system that automatically segments and labels glioblastoma-multiforme tumors in magnetic resonance images (MRI's) of the human brain is presented. The MRI's consist of T1-weighted, proton density, and T2-weighted feature images and are processed by a system which integrates knowledge-based (KB) techniques with multispectral analysis. Initial segmentation is performed by an unsupervised clustering algorithm. The segmented image, along with cluster centers for each class are provided to a rule-based expert system which extracts the intracranial region. Multispectral histogram analysis separates suspected tumor from the rest of the intracranial region, with region analysis used in performing the final tumor labeling. This system has been trained on three volume data sets and tested on thirteen unseen volume data sets acquired from a single MRI system. The KB tumor segmentation was compared with supervised, radiologist-labeled "ground truth" tumor volumes and supervised k-nearest neighbors tumor segmentations. The results of this system generally correspond well to ground truth, both on a per slice basis and more importantly in tracking total tumor volume during treatment over time.

Algorithms↗

PubMed1998

A vision-based technique for objective assessment of burn scars.

In this paper a method for the objective assessment of burn scars is proposed. The quantitative measures developed in this research provide an objective way to calculate elastic properties of burn scars relative to the surrounding areas. The approach combines range data and the mechanics and motion dynamics of human tissues. Active contours are employed to locate regions of interest and to find displacements of feature points using automatically established correspondences. Changes in strain distribution over time are evaluated. Given images at two time instances and their corresponding features, the finite element method is used to synthesize strain distributions of the underlying tissues. This results in a physically based framework for motion and strain analysis. Relative elasticity of the burn scar is then recovered using iterative descent search for the best nonlinear finite element model that approximates stretching behavior of the region containing the burn scar. The results from the skin elasticity experiments illustrate the ability to objectively detect differences in elasticity between normal and abnormal tissue. These estimated differences in elasticity are correlated against the subjective judgments of physicians that are presently the practice.

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PubMed1997

Medical image analysis with fuzzy models.

This paper updates several recent surveys on the use of fuzzy models for segmentation and edge detection in medical image data. Our survey is divided into methods based on supervised and unsupervised learning (that is, on whether there are or are not labelled data available for supervising the computations), and is organized first and foremost by groups (that we know of!) that are active in this area. Our review is aimed more towards 'who is doing it' rather than 'how good it is'. This is partially dictated by the fact that direct comparisons of supervised and unsupervised methods is somewhat akin to comparing apples and oranges. There is a further subdivision into methods for two- and three-dimensional data and/or problems. We do not cover methods based on neural-like networks or fuzzy reasoning systems. These topics are covered in a recently published companion survey by keller et al.

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PubMedDate not supplied

Left ventricular boundary detection from spatio-temporal volumetric computed tomography images.

This paper presents a new automatic technique for left ventricle boundary detection from a set of three-dimensional (3D) computed tomography (CT) volumetric cardiac images. The goals of this paper are to incorporate the temporal information into LV boundary detection, to link the shape modeling and LV boundary detection together, and to provide a compact representation of recovered LV boundaries to cardiac imaging. The proposed technique introduces spatio-temporal boundary detection and iterative model-based boundary refinement to left ventricular boundary extraction. The proposed technique has been applied to two sets of four-dimensional (4D) computed tomography images. Experimental results are compared with the manually edited images.

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PubMedDate not supplied

Scar assessment: current problems and future solutions.

Current problems in the assessment of scars are discussed. The concept of subjective and objective aspects of scar assessment is introduced. The patient's own view of the scar (the subjective component) can currently be assessed and may be very influential in determining the patient's quality of life, irrespective of the actual physical characteristics of the scar. The objective aspects of the scar, including size, shape, texture, and pliability, are currently difficult to measure. Although the Vancouver Scar Scale has been used as the standard for objective measurements, there are problems with both the validity and reliability of this instrument. Various imaging techniques may permit more reliable and accurate methods for measuring the quantitative aspects of scars.

Burns↗