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Previous studies have demonstrated that patients can be identified from 3-dimensional (3D) reconstructions of computed tomography (CT) or magnetic resonance imaging data of the brain or head and neck. This presents a privacy and security concern for scan data released to public data sets. It is unknown whether thermoplastic immobilization masks used for treatment planning in radiation therapy are sufficient to prevent facial recognition. Our study sought to evaluate whether patients with an immobilization mask could be identified on 3D reconstructions of scan data. Our study reconstructed 3D images from simulation CT (SIM-CT) scans of 35 patients and compared these to original patient photographs to test if the thermoplastic mask obfuscated facial features. Blind review from 4 facial recognition algorithms and a human (radiation oncologist) was evaluated for the ability to match 3D reconstructions of patients scans to patient images. The matching procedure was repeated against an expanded testing data set of the 35 patient photographs plus 13,233 facial photographs from the "Labeled Faces in the Wild" data set (13,268 photographs in total). Facial recognition algorithms were able to match a maximum of 83% (range, 60%-83%) of patients to the corresponding images. Radiation Oncologist blinded review correctly matched 80% of patients to the corresponding images. Ethnicity and facial hair were the most common reasons for patient mismatch. In the expanded testing data set, algorithms were also able to match a maximum of 83% (range, 57%-83%) of patients. The majority of patients were able to be identified through computer algorithm or human review even under a SIM-CT mask. These results suggest there is a potential privacy and security concern when SIM-CT data are released to publicly available data sets.

To predict the histologic grade of small clear cell renal cell carcinomas (ccRCCs) using texture analysis and machine learning algorithms. Fifty-two noncontrast (NC), 26 corticomedullary (CM) phase, and 35 nephrographic (NG) phase CTs of small (

Background: Brain stimulation is utilized to treat a variety of neurological disorders. Clinical brain stimulation technologies currently utilize charge-balanced pulse stimulation. The brain may better respond to other stimulation waveforms. This study was designed to evaluate the motor threshold of the brain to stimulation with various waveforms. Methods: Three stimulation waveforms were utilized on rats with surgically implanted brain electrodes: pulses, square waves, and random waveform. The peak-to-peak stimulation voltage was increased in a step-wise manner until motor signs were elicited. Results: The random waveform had the highest motor threshold with brain stimulation compared to the other waveforms. Random waveform stimulation reached maximum voltage without motor side effects while stimulating through both 1 and 8 electrodes. In contrast, the stimulation thresholds for motor side effects of the other two waveforms were on average less than half of the maximum voltage and lower for stimulation through 8 electrodes than stimulation through 1 electrode (p < 0.0005). Conclusion: The random waveform was better tolerated than the other waveforms and may allow for the use of higher stimulation voltage without side effects.

PURPOSE/OBJECTIVE(S) This study assessed trends in guideline-adherent chemoradiation therapy (GA-RT) for advanced-stage cervical cancer relative to Patient Protection and Affordable Care Act (ACA) implementation in the year 2010. MATERIALS/METHODS National Cancer Database patients treated with definitive chemoradiation for locally advanced cervical cancer (FIGO 2018 Stage IB3-IVA) from 2004-2016 were included. GA-RT was defined according to NCCN guidelines and included: 1) delivery of external beam radiation, 2) brachytherapy, and 3) chemotherapy, 4) no radical hysterectomy. Univariable and multivariable logistic regression were used to determine trends in GA-RT relative to the ACA and identify patient factors independently associated with GA-RT. Survival of GA-RT was also estimated using Kaplan-Meier analysis. RESULTS Thirty-seven thousand seven hundred and seventy-two patients met inclusion criteria (Pre-ACA: 16,169; Post-ACA: 21,673). A total of 33,116 patients had squamous cell carcinoma, and 4,626 patients had other histologies. Forty-five percent of patients were node positive. A total of 14.6% of patients were Stage I, 41.8% were Stage II, 36.4% were Stage III, and 7.9% were Stage IVA. Compared to commercial insurance, patients with Medicare had decreased odds of receiving GA-RT (OR = 0.65; 95% CI:0.61-0.69). Black patients (OR = 0.9; 95% CI:0.85-0.95) were associated with decreased odds of receiving GA-RT. Compared to squamous cell carcinoma, adenocarcinoma and carcinoma NOS were associated with decreased receipt of GA-RT (OR = 0.87; 95% CI: 0.80-0.93 and OR = 0.64; 95% CI: 0.55-0.75, respectively). Increasing T-stage was associated with greater receipt of GA-RT (stages 1B2 [OR = 1.89, 95% CI: 1.53-2.37], 2A2 [OR = 2.29, 95% CI: 1.81-2.92], 2B [OR = 2.1, 95% CI: 1.71-2.61], and 3B [OR = 1.47, 95% CI: 1.19-1.83]). Patients with nodal stage NX were less likely to receive GA-RT compared to N0 (OR = 0.6, 95% CI: 0.52-0.68). Increasing Charlson-Deyo score was associated with decreased receipt of GA-RT (OR = 0.86, 95% CI: 0.82-0.89). In multivariate analysis, all factors remained statistically significant except for race. The percentage of population that was guideline adherent significantly increased post-ACA (pre-ACA 28%; post-ACA 34%; P < 0.001). On univariate analysis, patients were 34% more likely to receive GA-RT after the ACA compared to before the ACA. In the multivariate model, receipt of GA-RT increased by 30% (P < 0.001). Adherence to treatment guidelines increased 2-year overall survival by 15% (GA 76%; Not GA 61%; P < 0.001). The post-ACA cohort was associated with increased 2-year overall survival compared to the pre-ACA cohort (pre-ACA 62%; post-ACA 69%; P < 0.001). CONCLUSION Patients were more likely to be guideline adherent and receive GA-RT after the implementation of the ACA. This increase in guideline adherence was also associated with an increase in 2-year survival, indicating implementation of the ACA may have improved patient outcomes. AUTHOR DISCLOSURE R.J. Thomas: None. D. Provenzano: None. S. Goyal: Partner; Center for Rheumatic Diseases. M. Loew: None. B. Long: None. M. Lopez-Acevedo: None. Y.J. Rao: None.

PURPOSE To provide an assessment of safety regarding high-dose-rate after-loading brachytherapy (HDR-BT) based on adverse events reported to the OpenFDA, an open access database maintained by the United States Food and Drug Administration (FDA). METHODS OpenFDA was queried for HDR-BT events between 1993 and 2019. A brachytherapist categorized adverse events (AEs) based on disease site, applicator, manufacturer, event type, dosimetry impact, and outcomes. Important findings are summarized. RESULTS 372 AEs were reported between 1993 and 2019, with a downwards trend after 2014. Nearly half of AEs (48.9%) were caused by a device malfunction, and 27.4% resulted in patient injury. Breast (49.2%) and Gyn (23.7%) were the most common disease sites of AEs. Applicator breaks cause the majority of AEs (64.2%) and breast balloon implants were the most common applicator to malfunction (38.7%). User error contributed to only 16.7% of events. 11.0% of events required repair of the afterloader. There were no reported staff injuries or patient deaths from an AE, however 24.7% of patients received resultant incorrect radiation dose, 16.4% required additional procedures to rectify the AE, and 3.0% resulted in unintended radiation to staff. CONCLUSION The OpenFDA database has shown a decreasing trend in AEs since 2014 for HDR-BT. Most AEs are not caused by user error and do not cause patient injury or incorrect radiation dose. Investigation into methods to prevent failures and improve applicators such as the breast balloon could improve safety. These results support the continued use of HDR-BT as a safe treatment modality for cancer.

Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic that has spread rapidly since December 2019. Real-time reverse transcription polymerase chain reaction (rRT-PCR) and chest computed tomography (CT) imaging both play an important role in COVID-19 diagnosis. Chest CT imaging offers the benefits of quick reporting, a low cost, and high sensitivity for the detection of pulmonary infection. Recently, deep-learning-based computer vision methods have demonstrated great promise for use in medical imaging applications, including X-rays, magnetic resonance imaging, and CT imaging. However, training a deep-learning model requires large volumes of data, and medical staff faces a high risk when collecting COVID-19 CT data due to the high infectivity of the disease. Another issue is the lack of experts available for data labeling. In order to meet the data requirements for COVID-19 CT imaging, we propose a CT image synthesis approach based on a conditional generative adversarial network that can effectively generate high-quality and realistic COVID-19 CT images for use in deep-learning-based medical imaging tasks. Experimental results show that the proposed method outperforms other state-of-the-art image synthesis methods with the generated COVID-19 CT images and indicates promising for various machine learning applications including semantic segmentation and classification., Comment: Accepted by IEEE Journal of Biomedical and Health Informatics (J-BHI)

To predict the histologic grade and type of small papillary renal cell carcinomas (pRCCs) using texture analysis and machine learning algorithms. This was a retrospective HIPAA-compliant study. 24 noncontrast (NC), 22 corticomedullary (CM) phase, and 24 nephrographic (NG) phase CTs of small (< 4 cm) surgically resected pRCCs were identified. Surgical pathology classified the tumors as low- or high-Fuhrman histologic grade and type 1 or 2. The axial image with the largest cross-sectional tumor area was exported and segmented. Six histogram and 31 texture (20 gray-level co-occurrences and 11 gray-level run-lengths) features were calculated for each tumor in each phase. Feature values in low- versus high-grade and type 1 versus 2 pRCCs were compared. Area under the receiver operating curve (AUC) was calculated for each feature to assess prediction of histologic grade and type of pRCCs in each phase. Histogram, texture, and combined histogram and texture feature sets were used to train and test three classification algorithms (support vector machine (SVM), random forest, and histogram-based gradient boosting decision tree (HGBDT)) with stratified shuffle splits and threefold cross-validation; AUCs were calculated for each algorithm in each phase to assess prediction of histologic grade and type of pRCCs. Individual histogram and texture features did not have statistically significant differences between low- and high-grade or type 1 and type 2 pRCCs across all phases. Individual features had low predictive power for tumor grade or type in all phases (AUC < 0.70). HGBDT was highly accurate at predicting pRCC histologic grade and type using histogram, texture or combined histogram and texture feature data from the CM phase (AUCs = 0.97–1.0). All algorithms had highest AUCs using CM phase feature data sets; AUCs decreased using feature sets from NC or NG phases. The histologic grade and type of small pRCCs can be predicted with classification algorithms using CM histogram and texture features, which outperform NC and NG phase image data. The accurate prediction of pRCC histologic grade and type may be able to further guide management of patients with small (< 4 cm) pRCCs being considered for active surveillance.

Background: Gulf War Illness (GWI) and Chronic Fatigue Syndrome (CFS) are two debilitating disorders that share similar symptoms of chronic pain, fatigue, and exertional exhaustion after exercise. Many physicians continue to believe that both are psychosomatic disorders and to date no underlying etiology has been discovered. As such, uncovering objective biomarkers is important to lend credibility to criteria for diagnosis and to help differentiate the two disorders. Methods: We assessed cognitive differences in 80 subjects with GWI and 38 with CFS by comparing corresponding fMRI scans during 2-back working memory tasks before and after exercise to model brain activation during normal activity and after exertional exhaustion, respectively. Voxels were grouped by the count of total activity into the Automated Anatomical Labeling (AAL) atlas and used in an &ldquo, ensemble&rdquo, series of machine learning algorithms to assess if a multi-regional pattern of differences in the fMRI scans could be detected. Results: A K-Nearest Neighbor (70%/81%), Linear Support Vector Machine (SVM) (70%/77%), Decision Tree (82%/82%), Random Forest (77%/78%), AdaBoost (69%/81%), Na&iuml, ve Bayes (74%/78%), Quadratic Discriminant Analysis (QDA) (73%/75%), Logistic Regression model (82%/82%), and Neural Net (76%/77%) were able to differentiate CFS from GWI before and after exercise with an average of 75% accuracy in predictions across all models before exercise and 79% after exercise. An iterative feature selection and removal process based on Recursive Feature Elimination (RFE) and Random Forest importance selected 30 regions before exercise and 33 regions after exercise that differentiated CFS from GWI across all models, and produced the ultimate best accuracies of 82% before exercise and 82% after exercise by Logistic Regression or Decision Tree by a single model, and 100% before and after exercise when selected by any six or more models. Differential activation on both days included the right anterior insula, left putamen, and bilateral orbital frontal, ventrolateral prefrontal cortex, superior, inferior, and precuneus (medial) parietal, and lateral temporal regions. Day 2 had the cerebellum, left supplementary motor area and bilateral pre- and post-central gyri. Changes between days included the right Rolandic operculum switching to the left on Day 2, and the bilateral midcingulum switching to the left anterior cingulum. Conclusion: We concluded that CFS and GWI are significantly differentiable using a pattern of fMRI activity based on an ensemble machine learning model.

Gulf War Illness (GWI) is a debilitating condition characterized by dysfunction of cognition, pain, fatigue, sleep, and diverse somatic symptoms with no known underlying pathology. As such, uncovering objective biomarkers such as differential regions of activity within a Functional Magnetic Resonance Imaging (fMRI) scan is important to enhance validity of the criteria for diagnosis. Symptoms are exacerbated by mild activity, and exertional exhaustion is a key complaint amongst sufferers. We modeled this exertional exhaustion by having GWI (n = 80) and sedentary control (n = 31) subjects perform submaximal exercise stress tests on two consecutive days. Cognitive differences were assessed by comparing fMRI scans performed during 2-Back working memory tasks before and after the exercise. Machine learning algorithms were used to identify differences in brain activation patterns between the two groups on Day 1 (before exercise) and Day 2 (after exercise). The numbers of voxels with t > 3.17 (corresponding to p < 0.001 uncorrected) were determined for brain regions defined by the Automated Anatomical Labeling (AAL) atlas. Data were divided 70:30 into training and test sets. Recursive feature selection identified twenty-nine regions of interest (ROIs) that significantly distinguished GWI from control on Day 1 and 28 ROIs on Day 2. Ten regions were present in both models between the two days, including right anterior insula, orbital frontal cortex, thalamus, bilateral temporal poles, and left supramarginal gyrus and cerebellar Crus 1. The models had 70% accuracy before exercise on Day 1 and 85% accuracy after exercise on Day 2, indicating the logistic regression model significantly differentiated subjects with GWI from the sedentary control group. Exercise caused changes in these patterns that may indicate the cognitive differences caused by exertional exhaustion. A second set of predictive models was able to classify previously identified GWI exercise subgroups START, STOPP, and POTS for both Days 1 and Days 2 with 67% and 69% accuracy respectively. This study was the first of its kind to differentiate GWI and the three sub-phenotypes START, STOPP, and POTS from a sedentary control using a logistic regression estimation method.

Breast cancer is the second leading cause of death for women in the U.S. Early detection of breast cancer is key to higher survival rates to breast cancer patients. We are investigating infrared (IR) thermography as a noninvasive adjunct to mammography for breast cancer screening. IR imaging is radiation-free, pain-free, and non-contact. Automatic segmentation of the breast area from the acquired full-size breast IR images will help limit the area for tumor search, as well as reduce the time and effort costs of manual hand segmentation. Autoencoder-like convolutional and deconvolutional neural networks (C-DCNN) had been applied to automatically segment the breast area in IR images in previous studies. In this study, we applied a state-of-the-art deep-learning segmentation model, MultiResUnet, which consists of an encoder part to capture features and a decoder part for precise localization. It was used to segment the breast area by using a set of breast IR images, collected in our clinical trials by imaging breast cancer patients and normal volunteers with our infrared camera (N2 Imager). The database we used has 450 images, acquired from 14 patients and 16 volunteers. We used a thresholding method to remove interference in the raw images and remapped them from the original 16-bit to 8-bit, and then cropped and segmented the 8-bit images manually. Experiments using leave-one-out cross-validation (LOOCV) and comparison with the ground-truth images by using Tanimoto similarity show that the average accuracy of MultiResUnet is 91.47%, which is about 2% higher than that of the autoencoder. MultiResUnet offers a better approach to segment breast IR images than our previous model.

Purpose To use machine-learning algorithms and blur measure (BM) operators to automatically detect motion blur in mammograms. Motion blur has been reported to reduce lesion detection performance and mask small abnormalities, resulting in failure to detect them until they reach more advanced stages. Automatic detection of blur could support the clinical decision-making process during the mammography exam by allowing for an immediate retake, thereby preventing unnecessary expense, time, and patient anxiety. Methods Blur was simulated mathematically to mimic the real blur seen in clinical practice. The blur point-spread-function (PSF) mask is generated by distributing pixel intensity of an image pixel moving under random motion within the range of blur effect (the maximum amount of tissue motion allowed). The random motion trajectory vector is generated on a super-sampled image frame to accommodate smaller substeps; the vector was then sampled on a regular pixel grid using subpixel linear interpolation to generate the blur PSF mask. This randomly generated motion trajectory is constrained by several factors: the effects of variations in tissue elasticity, imaging exposure time, and size of blur effect (motion boundary in millimeters) were examined. The blur mask is convolved with a mammogram to create blur. Five motion blur magnitudes (0.1, 0.25, 0.5, 1.0, and 1.5 mm) were simulated on 244 and 434 mammograms from the INbreast and DDSM databases, respectively. Blur was quantified using nine BM operators for each mammogram and at each blur level. The mammograms were assigned to training (70%) and testing (30%) datasets to train three machine-learning classifiers: Ensemble Bagged Trees, fine Gaussian SVM, and weighted KNN, to distinguish five levels of blurred from unblurred mammograms, using six-way classification. Results For the INbreast mammograms, the average classification accuracies were 87.7%, 85.7%, and 85.7% for Ensemble Bagged Trees, fine Gaussian SVM, and weighted KNN, respectively, and the average classification accuracies for DDSM were 93.5%, 93.6%, and 92.7% for Ensemble Bagged Trees, fine Gaussian SVM, and weighted KNN, respectively. Conclusions Preliminary results show the potential to detect simulated blur automatically using those methods. Although limited work has been done to quantify the effects of motion blur on radiologists' performance, there is evidence that motion blur might not be detected visually by a human observer and could negatively affect radiologists' lesion detection performance. As of this date, no other study has investigated the ability of machine-learning classifiers and BM operators to detect motion blur in mammograms.

This paper investigates optical coherence tomography (OCT) as an advanced, non-invasive method for 2D and 3D imaging of the surface and subsurface morphology of glass cultural heritage. The OCT system used is a commercial ThorLabs Ganymede II spectral domain-Fourier domain system with a 930 nm center wavelength, axial resolution of 4-6 μm, and lateral resolution of 8 μm. Results from model alkali silicate glass artificially aged at 90°C and 90% RH allow distinction of real features from artifacts produced by the highly reflective glass, and serve as a basis for interpretation of deterioration phenomena. Analytical results from historical glass artifacts are focused on a group of musical glass flutes created in Paris by Claude Laurent between 1807 and 1848. OCT images are compared to results of destructive analysis of the same samples and objects by scanning electron microscopy with backscattered electron imaging of cross-sections, as well as non-invasive light microscopy and NIR fiber optic reflectance spectrometry, the latter of which yields complementary molecular information in terms of water vibrations in hydrated glass.

Breast cancer is the second leading cause of death for women in the U.S. Early detection of breast cancer has been shown to be the key to higher survival rates for breast cancer patients. We are investigating infrared thermography as a noninvasive adjunctive to mammography for breast screening. Thermal imaging is safe, radiation-free, pain-free, and non-contact. Segmentation of breast area from the acquired thermal images will help limit the area for tumor search and reduce the time and effort needed for manual hand segmentation. Autoencoder-like convolutional and deconvolutional neural networks (C-DCNN) are promising computational approaches to automatically segment breast areas in thermal images. In this study, we apply the C-DCNN to segment breast areas from our thermal breast images database, which we are collecting in our clinical trials by imaging breast cancer patients with our infrared camera (N2 Imager). For training the C-DCNN, the inputs are 132 gray-value thermal images and the corresponding manually-cropped breast area images (binary masks to designate the breast areas). For testing, we input thermal images to the trained C-DCNN and the output after post-processing are the binary breast-area images. Cross-validation and comparison with the ground-truth images show that the C-DCNN is a promising method to segment breast areas. The results demonstrate the capability of C-DCNN to learn essential features of breast regions and delineate them in thermal images.

The Convolutional Neural Network (CNN) is a promising technique to detect breast cancer based on mammograms. Training the CNN from scratch, however, requires a large amount of labeled data. Such a requirement usually is infeasible for some kinds of medical image data such as mammographic tumor images. Because improvement of the performance of a CNN classifier requires more training data, the creation of new training images -- image augmentation – could be one solution to this problem. In this study, we applied the Generative Adversarial Network (GAN) to generate synthetic mammographic images from the Digital Database for Screening Mammography (DDSM). From the DDSM, we cropped two sets of regions of interest (ROIs) from the images: normal and abnormal (cancer/tumor) Those ROIs were used to train the GAN, and the GAN then generated synthetic images. To compare the GAN with the affine transformation augmentation methods, such as rotation, shifting, scaling, etc., we used six groups of ROIs (three simple groups: affine augmented, GAN synthetic, real (original), and three mixture groups of each pair of the three simple groups) for each to train a CNN classifier from scratch. And, we used real ROIs that were not used in training to validate classification outcomes. Our results show that, to classify the normal ROIs and abnormal (tumor) ROIs from DDSM, adding GAN-generated ROIs to the training data can reduce overfitting of the classifier. But the affine transformations performed slightly better than GAN. Therefore, GAN could be an optional augmentation approach. The images augmented by GAN or affine transformation cannot substitute entirely for real images to train CNN classifiers because the absence of real images in the training set will cause serious over-fitting with more training.

Atrial fibrillation is the most common cardiac arrhythmia. It is being effectively treated using the radiofrequency ablation (RFA) procedure, which destroys culprit tissue and creates scars that prevent the spread of abnormal electrical activity. Long-term success of RFA could be improved further if ablation lesions can be directly visualized during the surgery. We have shown that autofluorescence-based hyperspectral imaging (aHSI) can help to identify lesions based on spectral unmixing. We show that use of [Formula: see text]-means clustering, an unsupervised learning method, is capable of detecting RFA lesions without a priori knowledge of the lesions’ spectral characteristics. We also show that the number of spectral bands required for successful lesion identification can be significantly reduced, enabling the use of increased spectral bandwidth. Together, these findings can help with clinical implementation of a percutaneous aHSI catheter, since by reducing the number of spectral bands one can reduce hypercube acquisition and processing times, and by increasing the spectral width of individual bands one can collect more photons. The latter is of critical importance in low-light applications such as intracardiac aHSI. The ultimate goal of our studies is to help improve clinical outcomes for atrial fibrillation patients.

An abnormal thermogram has been shown to be a reliable indicator of increased risk of breast cancer. Numerical modeling techniques for thermography are proposed to quantify the complex relationships between the breast thermal behaviors and the underlying physiological/pathological conditions. Previous thermal modeling techniques did not account for gravity-induced elastic deformation arising from various body postures, nor did they suggest that a dynamic thermal procedure may be used to enhance clinical diagnosis. In this paper, 3D finite element method (FEM)-based thermal and elastic modeling techniques are developed to characterize comprehensively both the thermal and elastic properties of normal and tumorous breast tissues during static and dynamic thermography. In the steady state, gravity-induced breast deformation is found to cause an upper-lower asymmetric surface temperature contrast for sitting/standing up body posture, even though all the thermal and elastic properties are assumed uniform. Additionally, the tumor-induced surface temperature alterations are found to be caused primarily by shallow tumors and to be less sensitive to tumor size than to tumor depth. In the dynamic state, the breast exhibits distinctive temporal patterns that are associated with distinct thermal events: cold stress and thermal recovery induced by changes in the ambient temperature. Specifically, the tumor-induced thermal contrast shows an opposite initial change and delayed peak as compared with the deformation-induced thermal contrast. These findings are expected to provide a stronger foundation for, and greater specificity and precision in, thermographic diagnosis, and treatment of breast cancer.

In vivo autofluorescence hyperspectral imaging of moving objects can be challenging due to motion artifacts and to the limited amount of acquired photons. To address both limitations, we selectively reduced the number of spectral bands while maintaining accurate target identification. Several downsampling approaches were applied to data obtained from the atrial tissue of adult pigs with sites of radiofrequency ablation lesions. Standard image qualifiers such as the mean square error, the peak signal-to-noise ratio, the structural similarity index map, and an accuracy index of lesion component images were used to quantify the effects of spectral binning, an increased spectral distance between individual bands, as well as random combinations of spectral bands. Results point to several quantitative strategies for deriving combinations of a small number of spectral bands that can successfully detect target tissue. Insights from our studies can be applied to a wide range of applications.

Present noninvasive neuroimaging methods measure neuronal activity indirectly, via either cerebrovascular changes or extracranial measurements of electrical/magnetic signals. Recent studies have shown evidence that MRI may be used to directly and noninvasively map electrical activity associated with human brain activation, but results are inconclusive. Here, we show that MRI can detect cortical electrical activity directly. We use organotypic rat-brain cultures in vitro that are spontaneously active in the absence of a cerebrovascular system. Single-voxel magnetic resonance (MR) measurements obtained at 7 T were highly correlated with multisite extracellular local field potential recordings of the same cultures before and after blockade of neuronal activity with tetrodotoxin. Similarly, for MR images obtained at 3 T, the MR signal changed solely in voxels containing the culture, thus allowing the spatial localization of the active neuronal tissue.

PURPOSE To determine the importance of polyp size, orientation to the scan plane, collimation, scanner type (single or multislice helical), and radiation dose on computed tomography (CT) colonography computer-aided detection. MATERIALS AND METHODS Eight tissue-equivalent simulated polyps were placed into the interior of an air-filled acrylic tube placed within a water-filled box. Their sizes, expressed by diameter and height in millimeters, were 10 x 10, 10 x 7, 10 x 5, 10 x 3, 7 x 7, 7 x 5, 7 x 3, and 5 x 5. Detection of the polyps was performed by applying our prototype automated polyp detector software to 48 CT colonography data sets of the phantom acquired with different CT scanner settings. RESULTS We detected at least six of the eight polyps in 47 of 48 experiments. The two most frequently undetected polyps (7 x 7 and 5 x 5) had extreme eccentricity (their height was twice the radius of the base) and were most commonly missed for 90 degrees tube orientation, 5-mm collimation, and high table speed. False-positive detections occurred in only 5 of 48 experiments. CONCLUSION Clinically significant 10-mm polyps can be detected with 100% sensitivity in all orientations, doses, collimations, and modes that we examined.

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.

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.

In situ chemical imaging techniques are being developed to provide information on the spatial distribution of artists' pigments used in polychrome works of art such as paintings. The new methods include reflectance imaging spectroscopy and X-ray fluorescence mapping. Results from these new methods have extended the knowledge obtained from site-specific chemical analyses widely in use. While these mapping methods have aided in determining the distribution of pigments, there is a growing interest to develop methods capable of identifying and mapping organic paint binders as well. Near infrared (NIR) reflectance spectroscopy has been extensively used in the remote sensing field as well as in the chemical industry to detect organic compounds. NIR spectroscopy provides a rapid method to assay organics by utilizing vibrational overtones and combination bands of fundamental absorptions that occur in the mid-IR. Here we explore the utility of NIR reflectance imaging spectroscopy to map organic binders in situ by examining a series of panel paintings known to have been painted using distemper (animal skin glue) and tempera (egg yolk) binders as determined by amino acid analysis of samples taken from multiple sites on the panels. In this report we demonstrate the success in identifying and mapping these binders by NIR reflectance imaging spectroscopy in situ. Three of the four panel paintings from Cosimo Tura's The Annunciation with Saint Francis and Saint Louis of Toulouse (ca. 1475) are imaged using a highly sensitive, line-scanning hyperspectral imaging camera. The results show an animal skin glue binder was used for the blue skies and blue robe of the Virgin Mary, and egg yolk tempera was used for the red robes and brown landscape. The mapping results show evidence for the use of both egg yolk and animal skin glue in the faces of the figures. The strongest absorption associated with lipidic egg yolk features visually correlates with areas that appear to have white highlights. The results are in agreement with prior site-specific amino acid analysis, underscoring the synergy of both methods. The work here demonstrates that NIR reflectance imaging spectroscopy is a useful technique that can identify and map paint binding media based on differences in chemical composition.

The abnormal thermogram has been shown to be a reliable indicator of a high risk of breast cancer. Nevertheless, a major weakness of current infrared breast thermography is its poor sensitivity for deeper tumors. Numerical modeling for breast thermography provides an effective tool to investigate the complex relationships between the breast thermal behaviors and the underlying patho-physiological conditions. We have developed a set of new modeling techniques to take into account some subtle factors usually ignored in previous studies, such as gravity-induced elastic deformations of the breast, nonlinear elasticity of soft tissues, and dynamic behavior of thermograms. Conventional "forward problem" modeling cannot be used directly to improve tumor detectability, however, because the underlying tissue thermal properties are generally unknown. Therefore, we propose an "inverse problem" modeling technique that aims to estimate the tissue thermal properties from the breast surface thermogram. Our data suggest that the estimation of the tumor-induced thermal contrast can be improved significantly by using the proposed inverse problem solving techniques to provide the individual-specific thermal background, especially for deeper tumors. We expect the proposed new methods, taken together, to provide a stronger foundation for, and greater specificity and precision in, thermographic diagnosis, and treatment, of breast cancer.

This paper describes the development of novel computer-aided analysis algorithms to identify the language activation patterns at a certain Region of Interest (ROI) in Functional Magnetic Resonance Imaging (fMRI). Previous analysis techniques have been used to compare typical and pathologic activation patterns in fMRI images resulting from identical tasks but none of them analyzed activation topographically in a quantitative manner. This paper presents new analysis techniques and algorithms capable of identifying a pattern of language activation associated with localization related epilepsy. fMRI images of 64 healthy individuals and 31 patients with localization related epilepsy have been studied and analyzed on an ROI basis. All subjects are right handed with normal MRI scans and have been classified into three age groups (4-6, 7-9, 10-12 years). Our initial efforts have focused on investigating activation in the Left Inferior Frontal Gyrus (LIFG). A number of volumetric features have been extracted from the data. The LIFG has been cut into slices and the activation has been investigated topographically on a slice by slice basis. Overall, a total of 809 features have been extracted, and correlation analysis was applied to eliminate highly correlated features. Principal Component analysis was then applied to account only for major components in the data and One-Way Analysis of Variance (ANOVA) has been applied to test for significantly different features between normal and patient groups. Twenty Nine features have were found to be significantly different (p

The abnormal thermogram has been shown to be a reliable indicator of a high risk of breast cancer. Nevertheless, a major weakness of current infrared breast thermography is its poor sensitivity for deeper tumors. Numerical modeling for breast thermography provides an effective tool to investigate the complex relationships between the breast thermal behaviors and the underlying pathophysiological conditions. Conventional “forward problem” modeling cannot be used to directly improve the tumor detectability, however, because the underlying tissue thermal properties are generally unknown. Based on our new comprehensive forward modeling, we propose an “inverse problem” modeling technique that aims to estimate tissue thermal properties from the breast surface thermogram. Our data suggest that the estimation of tumor-induced thermal contrast can be significantly improved by using the proposed inverse problem solving techniques to provide the individual-specific thermal background, especially for deeper tumors.

Texture analysis for tissue characterization is a current area of optical coherence tomography (OCT) research. We discuss some of the differences between OCT systems and the effects those differences have on the resulting images and subsequent image analysis. In addition, as an example, two algorithms for the automatic recognition of bladder cancer are compared: one that was developed on a single system with no consideration for system differences, and one that was developed to address the issues associated with system differences. The first algorithm had a sensitivity of 73% and specificity of 69% when tested using leave-one-out cross-validation on data taken from a single system. When tested on images from another system with a different central wavelength, however, the method classified all images as cancerous regardless of the true pathology. By contrast, with the use of wavelet analysis and the removal of system-dependent features, the second algorithm reported sensitivity and specificity values of 87 and 58%, respectively, when trained on images taken with one imaging system and tested on images taken with another.

The authors present an efficient algorithm and the results of its application in simulating the three-dimensional (3-D) projection data resulting from a 3-D distribution of radioactivity. The algorithm was applied to a series of geometrical mathematical phantoms and to a realistic mathematical brain phantom. The authors simulated the projection data from a multidetector single-photon emission computed tomography (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 sheets, as well as the distribution of muscarinic cholinergic receptors in a realistic brain slice. Spheres were chosen as a model for brain structures such as caudate nucleus, thalamus, and cerebellum; sheets were selected as representing lateral cortical gray matter regions. The results of these simulations indicate the existence of significant qualitative and quantitative artifacts in reconstructed human brain images. >

The vast majority of bladder cancers originate within 600 m of the tissue surface, making optical coherence tomography OCT a potentially powerful tool for recognizing cancers that are not easily visible with current techniques. OCT is a new technology, how- ever, and surgeons are not familiar with the resulting images. Technol- ogy able to analyze and provide diagnoses based on OCT images would improve the clinical utility of OCT systems. We present an automated algorithm that uses texture analysis to detect bladder can- cer from OCT images. Our algorithm was applied to 182 OCT images of bladder tissue, taken from 68 distinct areas and 21 patients, to classify the images as noncancerous, dysplasia, carcinoma in situ CIS, or papillary lesions, and to determine tumor invasion. The re- sults, when compared with the corresponding pathology, indicate that the algorithm is effective at differentiating cancerous from noncancer- ous tissue with a sensitivity of 92% and a specificity of 62%. With further research to improve discrimination between cancer types and recognition of false positives, it may be possible to use OCT to guide endoscopic biopsies toward tissue likely to contain cancer and to avoid unnecessary biopsies of normal tissue. © 2008 Society of Photo-Optical

To quantify the complex relationships between (1) the temperature, and temperature differences, on the surface of the breast as recorded by infrared thermal imaging and (2) the underlying physiological and pathological factors, we have developed a dynamic finite element method for comprehensive modeling of both the thermal and elastic properties of normal and tumorous breast tissues. In the steady state, the gravity-induced deformation is found to cause markedly asymmetric surface temperatures even though all thermal-elastic properties are symmetrical. In the dynamic state, the time course of breast thermal imaging in cold-stress and thermal-recovery procedures is found to be useful in characterizing the origins of the thermal contrast on the breast surface. The tumor-induced thermal contrast has slower temporal behavior than the deformation-induced thermal contrast on the breast surface, which may lead to improvements in breast-tumor diagnosis.

The lining of the bladder is comprised of well defined layers that are clearly visible in optical coherence tomography (OCT) images of healthy bladder tissue. These layers are disturbed when cancerous cells are present. Consequently, recognition and classification of the layers of the bladder is very important in recognizing and staging bladder cancer. We present an algorithm that uses texture analysis, the k-means clustering algorithm, and edge detection software to segment OCT images of the lining of the bladder. We visually segmented 101 OCT images of bladder tissue, ran our algorithm on the images, and compared the results. The segmentation matched 59% of the time, matched reasonably well, with clear sources of error 29% of the time, and was incorrect 12% of the time. The combination of this algorithm with future texture analysis of the segmented layers will provide the tools required to perform a real-time optical biopsy using OCT

Automatic classification of the density of breast parenchyma is shown using a measure that is correlated to the human observer performance, and compared against the BI-RADS density rating. Increasingly popular in the United States, the Breast Imaging Reporting and Data System (BI-RADS) is used to draw attention to the increased screening difficulty associated with greater breast density; however, the BI-RADS rating scheme is subjective and is not intended as an objective measure of breast density. So, while popular, BI-RADS does not define density classes using a standardized measure, which leads to increased variability among observers. The adaptive thresholding technique is a more quantitative approach for assessing the percentage breast density, but considerable reader interaction is required. We calculate an objective density rating that is derived using a measure of local feature salience. Previously, this measure was shown to correlate well with radiologists' localization and discrimination of true positive and true negative regions-of-interest. Using conspicuous spatial frequency features, an objective density rating is obtained and correlated with adaptive thresholding, and the subjectively ascertained BI-RADS density ratings. Using 100 cases, obtained from the University of South Florida's DDSM database, we show that an automated breast density measure can be derived that is correlated with the interactive thresholding method for continuous percentage breast density, but not with the BI-RADS density rating categories for the selected cases. Comparison between interactive thresholding and the new salience percentage density resulted in a Pearson correlation of 76.7%. Using a four-category scale equivalent to the BI-RADS density categories, a Spearman correlation coefficient of 79.8% was found.

Increased blood glucose stimulates pancreatic β-cells and induces an exocytotic release of insulin. The β-cell, which contains ~10^4 insulin-containing granules, releases only a few percent of the granules during a given stimulus such as a meal. The temporal response function to a square wave increase in the concentration of glucose is characteristically biphasic. It is not known whether the granules exhibit random or directed migration patterns as a function of phase. Directed migration would suggest the development of an intracellular gradient directing the path and velocity of insulin granule movement. Our ongoing research investigates this process using manual morphometric analysis of electron micrographs of rat pancreatic β-cells. This is a tedious and time-consuming stereological process. Consequently, we have developed an automated algorithm for accurately segmenting and deriving granule counts, areas, and measuring distance to the plasma membrane. The method is a data-driven image processing approach that implements Mahalanobis classifiers to hierarchically classify pixel candidates and subsequently pixel aggregates as insulin granules. Granule cores and halos are classified independently and fused by intersecting the convex difference of granule halos with core candidates. Once fused, total and individual granule areas and distance metrics to the β-cell plasma membrane are obtained. This algorithm provides a rapid and accurate method for the determination of granule numbers, location, and potential gradients in the pancreatic β-cell under different experimental conditions.© (2005) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

We derive a general random-effects model to study the differences in spatial frequency features across class-type (true positive (TP) or true negative (TN) or False Positive (FP)), a sample of 40 mammogram cases, and 9 readers. We derive a measure of feature conspicuity or salience using visually inspired spatial frequency filters and mammogram regions of interest derived from eye-position data. Repeated-measures ANOVA is performed on the salient features obtained from all cases. We hypothesize that statistically significant differences in the average salience measure (D-score) are seen across both class-types and cases. We believe this to be useful for determining the similarity between images in training and testing sets used in CADx algorithm development or for a priori determination of test set difficulty. Further, we hypothesize that our salience measure is useful for distinguishing the spatial frequency bands most relied upon to distinguish true negative and true positive responses. This is useful in discerning the "bottoms-up" cues used to direct the point of gaze during mammogram inspection. These results indicate that our salience measure is useful as an indicator of image similarity and for separating TP & TN regions of interest.

In many cases the combined assessment of three-dimensional anatomical and functional images [single photon emission computed tomography (SPECT), positron emission tomography (PET), magnetic resonance imaging (MRI), and computed tomography (CT)] is necessary to determine the precise nature and extent of lesions. It is important, prior to performing the addition, subtraction, or any other combination of the images, that they be adequately aligned and registered either by experienced radiologists via visual inspection, mental reorientation and overlap of slices, or by an automated registration algorithm. To be useful clinically, the latter case requires validation. The human capacity to evaluate registration results visually is limited and time consuming. This paper describes an algorithmic procedure to provide proxy measures for human assessment that discriminate between badly misregistered pairs of brain images and those likely to be clinically useful. The new algorithm consists of four major steps: segmentation of brain and skin/air boundaries, contour extraction, computation of the principal axes, and computation of the registration quality measures from the contour volumes. The test data were MR and CT brain images. The results of the present study indicate that the use of a measure based on the combination of brain and skin contours and a principal axis function is a good first step to reduce the number of badly registered images reaching the clinician.

A median filtered pyramidal multiresolution (MFPM) image segmentation method for detecting and delineating compact objects was applied to single-photon emission computed tomography (SPECT) images. These SPECT images were obtained by scanning spheres from 1 to 54 ml in size and from 50% to 100% contrast settings. The algorithm performed accurately for large sphere (20 and 54 ml) and high contrast (90% and 100%) cases. As the size and contrast decrease, the accuracy of the method also decreases. Comparison of the MFPM method with adaptive thresholding with edge-preserving smoothing (ATEPS) indicated superior performance of the MFPM method. >

A medical virtual instrument has been developed to acquire and analyze the phonocardiogram (PCG), the heart sound and murmur signal. This instrument consists of a Pentium 200 computer running Windows95, equipped with LabVIEW software and a plug-in data acquisition board, and a two-channel custom designed bio-signal preamplifier. The bio-signal preamplifier allows the data acquisition board to acquire both the PCG and the electrocardiogram (ECG). LabVIEW software modules have been developed to create virtual instrument's front panels for the following functions: to manage patient information and data files, to capture and display current ECG and heart sound signals while saving or analyzing previous acquired signals, to perform the spectral and time-frequency analysis of the heart sounds and murmurs, and to review the previous recordings. This instrument can be used to display the PCG and to analyze the individual heart sound and murmur for the detection of heart valve diseases. It can also be used to analyze the carotid bruit for the diagnosis of carotid artery stenosis. This study demonstrated that a LabVIEW-based medical virtual instrument provides a low-cost, reliable, and flexible solution for data acquisition and analysis of the PCG and carotid bruit.

In this preliminary study the authors analyzed eyetrack data that was recorded while radiologists reviewed mammography images. The method of collecting the eyetrack data has been previously described by Barrett et al. (Acad. Radiol., vol. 1, p.40-5, 1994). The goal of this effort was to identify areas of microcalcifications from the eyetrack information. The identification of areas of microcalcification was accomplished using a Bayes classifier trained on images for which the presence of microcalcifications was verified by biopsies.

It is commonly thought that improvements in image S/N translate directly to improvements in fMRI sensitivity. This study demonstrates that improvements in image S/N by means of increased field strength, and the use of surface coils, does not translate to similar gains in temporal S/N, due to physiological noise. An analysis of the S/N dependence on signal strength for both the imaginary and real noise components is presented. The real noise component, which is lower than the imaginary component, is a significant contributor to temporal variations in single-shot fMRI procedures. The imaginary component becomes an important contributor to time series fluctuations in multi-shot or 3D techniques. In the experiments presented here, the relationship between image and temporal S/N is examined by modulating the signal strength by means of echo time stepping, field strength modulation, and RF coil comparison. The spatial and temporal noise contributions of the resting brain are characterized by comparing phantom, subject, and thermal noise measurements. The sensitivity of both spiral and EPI single-shot acquisition methods to physiologic and systematic noise is characterized. The results suggest that the fMRI sensitivity plateaus as image S/N is increased unless physiological noise is filtered out.© (2002) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Current functional MRI techniques are essentially based on detection of hemodynamic changes induced by neuronal activation. This study is an exploration of the feasibility of direct detection of neuronal current induced NMR phase and/or magnitude changes. Our analysis was based on the approximation that neurons exist in an infinite homogeneous conducting medium, and are represented by an infinitely long cylindrical conductor, carrying uniform current. Neuronal activation was modeled by a current dipole. Simulations were performed to evaluate the effects of the local neuronal magnetic fields on the MRI signal at 3T. The magnetic field changes and the corresponding phase changes induced from a current range of 2 nA to 100 (mu) A were estimated. The conductor diameter was varied from 10 micrometer to 1 mm corresponding to the sizes ranging from that of a single axon to that of a patch of functionally similar cortex. Current induced magnetic field effects were assessed as resolution and neuronal orientation with respect to Bo were modulated. Simulation results were compared with measurements obtained from a current phantom at 3T, for a current range of 10 - 100 (mu) A. Based on the dipole model formulated we found that neuronal currents can produce magnetic fields on the order of 1.3 pT; 0.0006 degree(s) to 9 nT; 4 degree(s) depending on the neuronal bundle diameter, orientation, and current capacity. The mechanism for signal changes is that of Bo shifts. This is fundamentally similar to that of BOLD contrast, but caused by current changes rather than susceptibility changes from changes in blood oxygenation. If the bundles are of random orientations within a voxel, the estimated fields and NMR magnitude changes (decrease in T2*) are on the order of present system detection levels of approximately 1nT; 0.5 degree(s) or 0.01% signal change. Conversely, if the bundle orientation is homogeneous, then neuronal current effects are above system detection levels. The spatial scale of the current distribution also determines the net phase shift and magnitude change. The simulation and phantom experiment results demonstrated the feasibility of using MRI to directly detect local magnetic field perturbations that can result from neuronal currents on the order of a few (mu) A. This study provides a simple model for the evaluation of the feasibility to directly measure neuronal currents with MRI. Additionally it gives a starting point for the design of appropriate imaging methods towards detecting low signal level neuronal currents. A more extensive modeling of cortical and neuronal geometry, tissue inhomogeneity, timing mechanisms, and current distributions, will provide further insight in the development of MRI experimental techniques.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Three-dimensional (3-D) color Doppler imaging of flow jets was performed to investigate the effects of flow rate and orifice size on jet volumes. Flow jets were generated using a flow model to simulate mitral regurgitation. This flow model consisted of a ventricular chamber, a valvular plate and an atrial chamber. Steady flow was driven through circular orifices having diameters of 2.5, 3.5, 4.5, and 6 mm, respectively, with flow rates of 5, 10, 15, 20, and 25 mL/s to form free jets in the atrial chamber. An ATL Ultramark 9 HDI system was used to perform 3-D color Doppler imaging of the flow jets. A transesophageal probe was rotated by a stepper motor to create 3-D color Doppler images of the jets. The color jet volumes for different hemodynamic conditions were measured and then compared with the theoretical predictions. Results showed that the jet volume estimated from the 3-D color Doppler was directly proportional to the flow rate and inversely proportional to the orifice size. The estimated jet volumes correlated well (r > 0.95) with theoretical predictions. This study supports the use of color jet volume as a parameter to quantify mitral regurgitation.

Medical images contain information (in two, three, and four dimensions) that is important to the clinician in diagnosis, evaluation, and treatment. New methodologies and tools make it possible to extract, interpret, integrate, and display that information very effectively. This paper describes some of the application areas, gives examples of the tools now available and under development, identifies the benefits and costs of the new approaches, examines the issues in evaluation and validation, and looks at opportunities for further improvements.

Blood vessels overlying one another at distinct depths (and hence appearing to intersect) in the sclera of the eye can be distinguished reliably from those that in fact do branch within the same depth, using only the information contained in a single photograph of the conjunctiva. That conclusion arises from extension of earlier work that qualitatively inferred relative depth of vessels. The current research was motivated by the need to quantify such inferences in terms of their sensitivities and robustness. A physics first principles model forms the basis for selection of features that capture blood vessel depth information. Features extracted from the image are shown to be useful in that effort; their utility is verified with phantoms that mimic the behavior of the conjunctiva and sclera. Because no special preparations are needed, the method works as well on archived images as on newly-acquired ones, and thus can be used in retrospective studies of images of the eye and other diffuse media.

MRI has been proposed as an alternative method to mammography for detecting and staging breast cancer. Recent studies have shown that architectural features of breast masses may be useful in improving specificity. Since fractal dimension (fd) has been correlated with roughness, and border roughness is an indicator of malignancy, the fd of the mass border is a promising architectural feature for achieving improved specificity. Previous methods of estimating the fd of the mass border have been unreliable because of limited data or overlay restrictive assumptions of the fractal model. We present preliminary results of a statistical approach in which a sample space of fd estimates is generated from a family of self-affine fractal models. The fd of the mass border is then estimated from the statistics of the sample space.

The conjunctiva is an ideal location to study and measure the morphology of the microcirculation, because access to blood vessels at this site is essentially non-invasive. Our efforts have been directed toward automating the labor-intensive process of collectingmorphological information from photographs and video images of the conjunctiva. Inprevious work we have developed a detailed model of the illumination/reflection processesthat result in a film or video image of the bulbar conjunctiva. Using information gained fromthis model, we have now extended our research toward the development of robustmicrovessel detection and tracking algorithms. Our modeling has shown that it is possibleto extract some relative three-dimensional information about blood vessels from their gray-scale profiles. Images of the conjunctiva, however, also exhibit significant variability in theirgray-scale data. We have adapted some fuzzy logic concepts to deal with this problem.These fuzzy logic algorithms have been very effective in detecting blood vessel points in

A new objective method for hearing threshold testing, parametric modeling, is proposed in this paper. The fifth-order autoregressive models are estimated for the averaged BAEP records at a series of stimulus levels. The normalized prediction error enor is chosen as the hearing threshold measure. For normal hearing subjects, the values of enor are very different between the control condition and the click stimulus. For abnormal patients with different hearing loss problems, the estimated hearing thresholds in terms of enor show a good agreement with the physicians' assessment.