Biomedical subjects

Alan H Baydush

Publications and source records attributed to Alan H Baydush.

6 recordsLinked to original sources

PubMed2006

Characterization of difference of Gaussian filters in the detection of mammographic regions.

In this article, we present a characterization of the effect of difference of Gaussians (DoG) filters in the detection of mammographic regions. DoG filters have been used previously in mammographic mass computer-aided detection (CAD) systems. As DoG filters are constructed from the subtraction of two bivariate Gaussian distributions, they require the specification of three parameters: the size of the filter template and the standard deviations of the constituent Gaussians. The influence of these three parameters in the detection of mammographic masses has not been characterized. In this work, we aim to determine how the parameters affect (1) the physical descriptors of the detected regions, (2) the true and false positive rates, and (3) the classification performance of the individual descriptors. To this end, 30 DoG filters are created from the combination of three template sizes and four values for each of the Gaussians' standard deviations. The filters are used to detect regions in a study database of 181 craniocaudal-view mammograms extracted from the Digital Database for Screening Mammography. To describe the physical characteristics of the identified regions, morphological and textural features are extracted from each of the detected regions. Differences in the mean values of the features caused by altering the DoG parameters are examined through statistical and empirical comparisons. The parameters' effects on the true and false positive rate are determined by examining the mean malignant sensitivities and false positives per image (FPpI). Finally, the effect on the classification performance is described by examining the variation in FPpI at the point where 81% of the malignant masses in the study database are detected. Overall, the findings of the study indicate that increasing the standard deviations of the Gaussians used to construct a DoG filter results in a dramatic decrease in the number of regions identified at the expense of missing a small number of malignancies. The sharp reduction in the number of identified regions allowed the identification of textural differences between large and small mammographic regions. We find that the classification performances of the features that achieve the lowest average FPpI are influenced by all three of the parameters.

Algorithms↗

PubMed2005

Predicting radiotherapy-induced cardiac perfusion defects.

The purpose of this work is to compare the efficacy of mathematical models in predicting the occurrence of radiotherapy-induced left ventricular perfusion defects assessed using single-photon emission computed tomography (SPECT). The basis of this study is data from 73 left-sided breast/ chestwall patients treated with tangential photon fields. The mathematical models compared were three commonly used parametric models [Lyman normal tissue complication probability (LNTCP), relative serialty (RS), generalized equivalent uniform dose (gEUD)] and a nonparametric model (Linear discriminant analysis--LDA). Data used by the models were the left ventricular dose--volume histograms, or SPECT-based dose-function histograms, and the presence/absence of SPECT perfusion defects 6 months postradiation therapy (21 patients developed defects). For the parametric models, maximum likelihood estimation and F-tests were used to fit the model parameters. The nonparametric LDA model step-wise selected features (volumes/function above dose levels) using a method based on receiver operating characteristics (ROC) analysis to best separate the groups with and without defects. Optimistic (upper bound) and pessimistic (lower bound) estimates of each model's predictive capability were generated using ROC curves. A higher area under the ROC curve indicates a more accurate model (a model that is always accurate has area = 1). The areas under these curves for different models were used to statistically test for differences between them. Pessimistic estimates of areas under the ROC curve using dose-volume histogram/ dose-function histogram inputs, in order of increasing prediction accuracy, were LNTCP (0.79/0.75), RS (0.80/0.77), gEUD (0.81/0.78), and LDA (0.84/0.86). Only the LDA model benefited from SPECT-based regional functional information. In general, the LDA model was statistically superior to the parametric models. The LDA model selected as features the left ventricular volumes above approximately 23 Gy (V23), essentially volume in field, and 33 Gy (V33), as best separating the groups with and without defects. In conclusion, the nonparametric LDA model appears to be a more accurate predictor of radiotherapy-induced left ventricular perfusion defects than commonly used parametric models.

Dose-Response Relationship, Radiation↗

PubMed2004

Incorporation of an iterative, linear segmentation routine into a mammographic mass CAD system.

In previous research, we have developed a computer-aided detection (CAD) system designed to detect masses in mammograms. The previous version of our system employed a simple but imprecise method to localize the masses. In this research, we present a more robust segmentation routine for use with mammographic masses. Our hypothesis is that by more accurately describing the morphology of the masses, we can improve the CAD system's ability to distinguish masses from other mammographic structures. To test this hypothesis, we incorporated the new segmentation routine into our CAD system and examined the change in performance. The developed iterative, linear segmentation routine is a gray level-based procedure. Using the identified regions from the previous CAD system as the initial seeds, the new segmentation algorithm refines the suspicious mass borders by making estimates of the interior and exterior pixels. These estimates are then passed to a linear discriminant, which determines the optimal threshold between the interior and exterior pixels. After applying the threshold and identifying the object's outline, two constraints on the border are applied to reduce the influence of background noise. After the border is constrained, the process repeats until a stopping criterion is reached. The segmentation routine was tested on a study database of 183 mammographic images extracted from the Digital Database for Screening Mammography. Eighty-three of the images contained 50 malignant and 50 benign masses; 100 images contained no masses. The previously developed CAD system was used to locate a set of suspicious regions of interest (ROIs) within the images. To assess the performance of the segmentation algorithm, a set of 20 features was measured from the suspicious regions before and after the application of the developed segmentation routine. Receiver operating characteristic (ROC) analysis was employed on the ROIs to examine the discriminatory capabilities of each individual feature before and after the segmentation routine. A statistically significant performance increase was found in many of the individual features, particularly those describing the mass borders. To examine how the incorporation of the segmentation routine affected the performance of the overall CAD system, free-response ROC (FROC) analysis was employed. When considering only malignant masses, the FROC performance of the system with the segmentation routine appeared better than the previous system. When detecting 90% of the malignant masses, the previous system achieved 4.9 false positives per image (FPpI) compared to the post-segmentation system's 4.2 FPpI. At 80% sensitivity, the respective FPpI were 3.5 and 1.6.

Algorithms↗

PubMed2004

Penalized likelihood fluence optimization with evolutionary components for intensity modulated radiation therapy treatment planning.

A novel iterative penalized likelihood algorithm with evolutionary components for the optimization of beamlet fluences for intensity modulated radiation therapy (IMRT) is presented. This algorithm is designed to be flexible in terms of the objective function and automatically escalates dose, as long as the objective function increases and all constraints are met. For this study, the objective function employed was the product of target equivalent uniform dose (EUD) and fraction of target tissue within set homogeneity constraints. The likelihood component of the algorithm iteratively attempts to minimize the mean squared error between a homogeneous dose prescription and the actual target dose distribution. The updated beamlet fluences are then adjusted via a quadratic penalty function that is based on the dose-volume histogram (DVH) constraints of the organs at risk. The evolutionary components were included to prevent the algorithm from converging to a local maximum. The algorithm was applied to a prostate cancer dataset, with especially difficult DVH constraints on bladder, rectum, and femoral heads. Dose distributions were generated for manually selected sets of three-, four-, five-, and seven-field treatment plans. Additionally, a global search was performed to find the optimal orientations for an axial three-beam plan. The results from this optimal orientation set were compared to results for manually selected orientation (gantry angle) sets of 3- (0 degrees, 90 degrees, 270 degrees), 4- (0 degrees, 90 degrees, 180 degrees, 270 degrees), 5- (0 degrees, 50 degrees, 130 degrees, 230 degrees, 310 degrees), and 7- (0 degrees, 40 degrees, 90 degrees, 140 degrees, 230 degrees, 270 degrees, 320 degrees) field axial treatment plans. For all the plans generated, all DVH constraints were met and average optimization computation time was approximately 30 seconds. For the manually selected orientations, the algorithm was successful in providing a relatively homogeneous target dose distribution, while simultaneously satisfying dose-volume limits by diverting dose away from proximal critical structures. The global search for an optimal three-beam orientation set yielded gantry angles of 70 degrees, 170 degrees, and 320 degrees. The EUD for this orientation set was 58 Gy, with 96% of the target within the set upper and lower limits. In comparison, optimized EUDs for the manually selected orientation sets of three, four, five and seven beams were 52.3, 52.6, 56.9, and 61.3 Gy, respectively. The orientation optimized three-beam plan yielded higher EUDs than the manually selected three-, four-, and five-beam plans, but lower EUDs than the seven-beam plan. In conclusion, a novel penalized likelihood algorithm with evolutionary components has successfully been implemented to optimize beamlet fluences for IMRT. Initial results are promising for dose conformity and uniformity of dose to target. When combined with optimal beam orientation selection for prostate cancer treatment planning, the results indicate that plans with a small number of optimized beam orientations achieve results comparable to those with a larger number of conventionally oriented beams.

Bone and Bones↗

PubMed2003

Computer aided detection of masses in mammography using subregion Hotelling observers.

We propose to investigate the use of the subregion Hotelling observer for the basis of a computer aided detection scheme for masses in mammography. A database of 1320 regions of interest (ROIs) was selected from the DDSM database collected by the University of South Florida using the Lumisys scanner cases. The breakdown of the cases was as follows: 656 normal ROIs, 307 benign ROIs, and 357 cancer ROIs. Each ROI was extracted at a size of 1024 x 1024 pixels and sub-sampled to 128 x 128 pixels. For the detection task, cancer and benign cases were considered positive and normal was considered negative. All positive cases had the lesion centered in the ROI. We chose to investigate the subregion Hotelling observer as a classifier to detect masses. The Hotelling observer incorporates information about the signal, the background, and the noise correlation for prediction of positive and negative and is the optimal detector when these are known. For our study, 225 subregion Hotelling observers were set up in a 15 x 15 grid across the center of the ROIs. Each separate observer was designed to "observe," or discriminate, an 8 x 8 pixel area of the image. A leave one out training and testing methodology was used to generate 225 "features," where each feature is the output of the individual observers. The 225 features derived from separate Hotelling observers were then narrowed down by using forward searching linear discriminants (LDs). The reduced set of features was then analyzed using an additional LD with receiver operating characteristic (ROC) analysis. The 225 Hotelling observer features were searched by the forward searching LD, which selected a subset of 37 features. This subset of 37 features was then analyzed using an additional LD, which gave a ROC area under the curve of 0.9412 +/- 0.006 and a partial area of 0.6728. Additionally, at 98% sensitivity the overall classifier had a specificity of 55.9% and a positive predictive value of 69.3%. Preliminary results suggest that using subregion Hotelling observers in combination with LDs can provide a strong backbone for a CAD scheme to help radiologists with detection. Such a system could be used in conjunction with CAD systems for false positive reduction.

Algorithms↗

PubMed2003

Chest radiography: optimization of X-ray spectrum for cesium iodide-amorphous silicon flat-panel detector.

PURPOSE: To ascertain the optimum x-ray spectrum for chest radiography with a cesium iodide-amorphous silicon flat-panel detector. MATERIALS AND METHODS: End points for optimization included the ratio of tissue contrast to bone contrast and a figure of merit (FOM) equal to the square of the signal-to-noise ratio of tissue divided by incident exposure to the patient. Studies were conducted with both computer spectrum modeling and experimental measurement in narrow-beam and full-field exposure conditions for four tissue thicknesses (8-32 cm). Three parameters that affect spectra were considered: the atomic number (Z) of filter material (Z = 13, 26, 29, 42, 50, 56, 64, 74, and 82), kilovoltage (from 50 to 150 kVp), and filter thickness (from 0.25 to 2.00 half-value layer [HVL]). RESULTS: Computer modeling and narrow-beam experimental data showed similar trends for the full range of parameters evaluated. Spectrum model results showed that copper filtration at 120 kVp or more was optimum for FOM. The ratio of contrasts showed a trend to be higher with higher kilovoltage and only a minor variation with filter material. Full-field experimental results, which reflect the added contribution of x-ray scatter, differed in magnitude but not trends from the narrow-beam data in all cases except the ratio of contrasts in the mediastinum. CONCLUSION: The best performance overall, including both FOM and ratio of contrasts, was at 120 kVp with 1-HVL copper filtration (0.2 mm). With this beam spectrum and an increase in tube output (ie, milliampere seconds) of about 50%, a chest radiograph can be obtained with image quality approximately equal to that with a conventional spectrum but with about 25% less patient exposure.

Cesium↗