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Biomedical subjects

M H Loew

Publications and source records attributed to M H Loew.

10 recordsLinked to original sources
PubMed1999

Discrimination of MR images of breast masses with fractal-interpolation function models.

RATIONALE AND OBJECTIVES: The authors evaluated the feasibility of using statistical fractal-dimension features to improve discrimination between benign and malignant breast masses at magnetic resonance (MR) imaging. MATERIALS AND METHODS: The study evaluated MR images of 32 malignant and 20 benign breast masses from archived data at the University of Pennsylvania Medical Center. The test set included four cases that were difficult to evaluate on the basis of border characteristics. All diagnoses had been confirmed at excisional biopsy. The fractal-dimension feature was computed as the mean of a sample space of fractal-dimension estimates derived from fractal interpolation function models. To evaluate the performance of the fractal-dimension feature, the classification effectiveness of five expert-observer architectural features was compared with that of the fractal dimension combined with four expert-observer features. Feature sets were evaluated with receiver operating characteristic analysis. Discrimination analysis used artificial neural networks and logistic regression. Robustness of the fractal-dimension feature was evaluated by determining changes in discrimination when the algorithm parameters were perturbed. RESULTS: The combination of fractal-dimension and expert-observer features provided a statistically significant improvement in discrimination over that achieved with expert-observer features alone. Perturbing selected parameters in the fractal-dimension algorithm had little effect on discrimination. CONCLUSION: A statistical fractal-dimension feature appears to be useful in distinguishing MR images of benign and malignant breast masses in cases where expert radiologists may have difficulty. The statistical approach to estimating the fractal dimension appears to be more robust than other fractal measurements on data-limited medical images.

Algorithms↗
PubMed1998

Use of the fast Hartley transform for three-dimensional dose calculation in radionuclide therapy.

Effective radioimmunotherapy may depend on a priori knowledge of the radiation absorbed dose distribution obtained by trace imaging activities administered to a patient before treatment. A new, fast, and effective treatment planning approach is developed to deal with a heterogeneous activity distribution. Calculation of the three-dimensional absorbed dose distribution requires convolution of a cumulated activity distribution matrix with a point-source kernel; both are represented by large matrices (64 x 64 x 64). To reduce the computation time required for these calculations, an implementation of convolution using three-dimensional (3-D) fast Hartley transform (FHT) is realized. Using the 3-D FHT convolution, absorbed dose calculation time was reduced over 1000 times. With this system, fast and accurate absorbed dose calculations are possible in radioimmunotherapy. This approach was validated in simple geometries and then was used to calculate the absorbed dose distribution for a patient's tumor and a bone marrow sample.

Antibodies, Monoclonal↗
PubMed1997

Estimating fractal dimension with fractal interpolation function models.

Fractal dimension (fd) is a feature which is widely used to characterize medical images. Previously, researchers have shown that fd separates important classes of images and provides distinctive information about texture. We analyze limitations of two principal methods of estimating fd: box-counting (BC) and power spectrum (PS). BC is ineffective when applied to data-limited, low-resolution images; PS is based on a fractional Brownian motion (fBm) model-a model which is not universally applicable. We also present background information on the use of fractal interpolation function (FIF) models to estimate fd of data which can be represented in the form of a function. We present a new method of estimating fd in which multiple FIF models are constructed. The mean of the fd's of the FIF models is taken as the estimate of the fd of the original data. The standard deviation of the fd's of the FIF models is used as a confidence measure of the estimate. We demonstrate how the new method can be used to characterize fractal texture of medical images. In a pilot study, we generated plots of curvature values around the perimeters of images of red blood cells from normal and sickle cell subjects. The new method showed improved separation of the image classes when compared to BC and PS methods.

Fractals↗
PubMed1996

Modeling and simulation of illumination effects for evaluation of microvessels of the conjunctiva.

We present the development of a comprehensive model that was undertaken to determine the relationships between the components of an image and the light intensity values present in the image of the microvessels of translucent tissues such as the bulbar conjunctiva. Experiments were conducted during the modeling process by use of a cylindrical microvessel embedded in a diffuse medium (phantom) on a reflecting background to affirm model components and simulations. The three-dimensional model was reduced to a single illumination plane with four regions of interest and modeled as Lambertian radiators and surfaces. The modeling showed that the top of the cylinder and its immediate vicinity are diffuse reflectors of light from the source plus light reflected from the background. The limbus of the cylinder is a diffuse reflector of the source and background illumination and a specular reflector of background reflections that achieve a high grazing angle with the cylinder. The immediate vicinity of the cylinder receives direct illumination from the source, but the light is partially obscured by the cylinder. The region beyond the shadow of the cylinder is a diffuse reflector of the overhead light. The diffuse medium additionally reflects the source and also attenuates the illumination reaching the other compo- rents of the scene. The direct and reflected illumination at each region of the model was calculated by use of specific geometric relationships. To verify those calculations, we analyzed a video simulation for the effects of different illumination conditions and their contributing elements. Intensity values were calculated from the relative reflectivity data determined from the video signals. The illumination values at the points along the line at the meridian of the cylinder were due to its reflectivity and also that of the medium. Similarly, the values of points distant from the shadow of the cylinder were due to the reflectivity of the background and the medium. The excellent agreement between the model and the phantom provides a foundation for the detection and precise measurement of microvessel dimensions within a diffuse medium. The additional ability to compute relative depth, from a single view, also permits discrimination between neighboring microvessels in complex images.

Computer Simulation↗
PubMed1995

Threshold estimation in single photon emission computed tomography and planar imaging for clinical radioimmunotherapy.

Thresholding is the most widely used organ or tumor segmentation technique used in single photon emission computed tomography (SPECT) and planar imaging for monoclonal antibodies. Selecting the optimal threshold requires a priori knowledge (volumes from CT or magnetic resonance) for the size and contrast level of the organ in question. Failure to select an optimal threshold leads to overestimation or underestimation of the volume and, subsequently, the organ-absorbed dose value in radio-immunotherapy. To investigate this threshold selection problem, we performed a phantom experiment using six lucite spheres ranging from 1 to 117 ml and filled with a uniform activity of 1 microCi/ml Tc-99m. These spheres were placed at the center and off-center locations of a Jasczsak phantom and scanned with a three-headed gamma camera in SPECT and planar modes. Target-nontarget (T:NT) ratios were changed by adding the appropriate activity to the background. A threshold search algorithm with an interpolative background correction was applied to sphere images. This algorithm selects a threshold that minimizes the difference between the true and measured volumes (SPECT) or areas (planar). It was found that for spheres equal to or larger than 20 ml [diameter (D) > 38 mm] and T:NT ratios higher than 5:1, mean thresholds at 42% for SPECT and 38% for planar imaging yielded minimum image segmentation errors, which is in agreement with current literature. However, for small T:NT ratios (< 5:1), the threshold values as high as 71% for SPECT and 85% for planar imaging were substantially different than those fixed thresholds for large spheres (D > 38 mm). Hence, the use of fixed thresholds in low contrasts and with tumor and organ sizes of clinical interest (25 < or = D < or = 50 mm) may result in limited volume estimation accuracy. Therefore, we have provided the investigator a method to obtain the threshold values in which the proper threshold can be selected based on the organ and tumor size and image contrast. By measuring and calibrating the proper threshold value derived through machine-specific phantom measurements, a more accurate volume and activity quantitation can be performed. This, in turn, will provide tumor-absorbed dose optimization and greater accuracy in the measurement of potentially subacute, toxic absorbed doses to normal organs for patients undergoing radioimmunotherapy.

Algorithms↗
PubMed1991

Three-dimensional simulations of multidetector point-focusing SPECT imaging.

We have applied an efficient algorithm for mathematically simulating the three-dimensional (3-D) response of a SPECT imaging system with a depth-dependent 3-D point spread function (3-DPSF). The input object whose reconstructed image is to be simulated is restricted to a binary map; more complex objects may be treated as linear combinations of binary maps. The 3-D convolution reduces to a sequence of additions of a 3-D line spread function (3-DLSF), appropriately translated, to the 3-D response. We have simulated the projection data from a multidetector SPECT system with point-focusing collimators. The simulated projection data were then reconstructed using the manufacturer's software. The objects simulated included simple geometrical solids such as spheres and cylinders, as well as the distribution of muscarinic cholinergic receptors in a realistic brain slice. The results of these simulations indicate the existence of significant qualitative and quantitative artifacts in reconstructed human brain images.

Algorithms↗
PubMedDate not supplied

A clinical evaluation of contrast-detail analysis for ultrasound images.

We report on the reproducibility of human observers' vanishing detection thresholds for visual targets in contrast-detail (C/D) analysis of ultrasound B-mode images. The images used in this study contain visual targets which are circular cross sections of constant-contrast conical structures in the C/D phantom. The vanishing threshold diameters for these targets vary as a function of the perceived size of the imaged target, target-to-background contrast, image noise content, and reproducibility of the decision levels of human observers for repeated observations. Our study indicates that the determination of absolute vanishing threshold diameter values for several targets of different contrast by human observers yields a high degree of error that is not predicted by existing theoretical assumptions based on a static threshold detector. We find that systematic error is introduced by the observers during the course of the experiment and that the levels of sensitivity of the observers differ widely at all times, and increase the amount of total observer error. These results suggest that, due to the large total observer error, C/D analysis may be impractical in a clinical environment, unless there is access to a team of observers specifically and extensively trained in this task. We suggest that a computer-based observer may be more reliable for the objective performance of contrast-detail analysis as a method for evaluating ultrasound image quality and comparison of imaging systems.

Humans↗