Is it blur or is it texture? An analysis of computational blur and texture measures commonly used in medical image analysis.

Purpose: In this work, we endeavor to investigate how texture information may contribute to the response of a blur measure (BM) with motivation rooted in mammography. This is vital as the interpretation of the BM is typically not evaluated with respect to texture present in an image. We are particularly concerned with lower scales of blur (≤1 mm) as this blur is least likely to be detected but can still have a detrimental effect on detectability of microcalcifications. Approach: Three sets of linear models, where BM response was modeled as a linear combination of texture information determined by texture measures (TMs), were constructed from three different datasets of equal-blur-level images; one of computer-generated mammogram-like clustered lumpy background (CLB) images and two image sets derived from the Brodatz texture images. The linear models were refined by removing those TMs that are not significantly non-zero across all three datasets for each BM. We use five levels of Gaussian blur to blur the CLB images and assess the ability of the BMs and TMs to separate the images based on blur level. Results: We found that many TMs used frequently in the reduced linear models, mimicked the structure of the BMs that they modeled. Surprisingly, while none of the BMs could separate the CLB images across all levels of blur, a group of TMs could. These TMs occurred infrequently in the reduced linear models meaning that they rely on different information compared with that used by the BMs. Conclusion: These results confirm our hypothesis that BMs can be influenced by texture information in an image. That a subset of TMs performed better than all BMs on the blur classification problem with the CLB images further shows that conventional BMs may not be the optimal tool for blur classification in mammogram images.

Brief history of Image Processing at SPIE Medical Imaging.

Image processing has contributed greatly to the clinical applications of medical imaging. Many of the major developments have been stimulated by and reported at the Image Processing (IP) conference held annually as part of the SPIE Medical Imaging meeting. The evolution, focus, and impact of the IP conference is reviewed.

Unmasking a Privacy Concern: Potential Identification of Patients in an Immobilization Mask from 3-Dimensional Reconstructions of Simulation Computed Tomography.

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.

Adverse events of after-loading high dose rate brachytherapy reported to the United States Food and Drug Administration (FDA).

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.

Threshold for Tonic Motor Effects from Random Waveform in a Rat Experimental Model of Frontal Cortex Stimulation.

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.

Heat transfer analysis and resolution quantification of active dynamic thermography through human skin.

OBJECTIVES: Active dynamic thermography (ADT) is a non-contact imaging technique that characterizes non-homogeneities in thermal conductance through objects as a response to applied energy stimulus. The aim of this study was to (i) develop a heat transfer model to define the relationship between thermal stimulation and resolution and (ii) empirically quantify the resolution an ADT imaging system can detect through a range of depths of human skin. MATERIALS AND METHODS: A heat transfer model was developed to describe a thermally non-conductive object below a sheet of skin. The size and depth of the object were varied to simulate wound conditions, while the intensity and duration of thermal stimulation were varied to define stimulation parameters. The model was solved by numerical analysis. For ex vivo experimentation, freshly excised human pannus tissue was cut into sheets of thickness 2.54-6.35 × 10-4 m (0.010-0.025vinches) for a total of 48 grafts from 12 patients. Grafts were placed over a 3D printed resolution target with objects ranging from 0.445-0.125 LP/mm. Stimulation from a 300 W halogen lamp array was applied for 0.5-14 seconds for a total of 480 experiments. RESULTS: ADT resolved a peak of 0.428 ± 0.025 LP/mm for 2.54 × 10-4 m (0.010 inches) skin thickness, 0.384 ± 0.030 LP/mm for 3.81 × 10-4 m (0.015 inches), 0.325 ± 0.042 LP/mm for 5.08 × 10-4 m (0.020 inches) and 0.249 ± 0.057 LP/mm for 6.35 × 10-4 m (0.025 inches) skin thickness. Additionally, it was determined that the ideal duration of stimulation energy with a 300 W stimulation system was 4 seconds for 2.54 × 10-4 m, 6 seconds for 3.81 × 10-4 m, 8 seconds for 5.08 × 10-4 m, and 14 seconds for 6.35 × 10-4 m skin thickness. CONCLUSIONS: This study has characterized the correlation between thermal stimulus input and resolvable object size and depth for ADT. Through ex vivo experimentation it has also quantified the functional imaging depth to below the sub-cutis, beyond that of conventional imaging techniques. Lasers Surg. Med. © 2018 Wiley Periodicals, Inc.

Modeling static and dynamic thermography of the human breast under elastic deformation.

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.

Wavelet analysis enables system-independent texture analysis of optical coherence tomography images.

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.

Dynamic thermal modeling of the normal and tumorous breast under elastic deformation.

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.

Computer recognition of cancer in the urinary bladder using optical coherence tomography and texture analysis.

The vast majority of bladder cancers originate within 600 microm 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, however, and surgeons are not familiar with the resulting images. Technology 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 cancer 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 results, when compared with the corresponding pathology, indicate that the algorithm is effective at differentiating cancerous from noncancerous 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.

Mapping spatiotemporal diffusion inside the human brain using a numerical solution of the diffusion equation.

Diffusion is an important mechanism for molecular transport in living biological tissues. Diffusion magnetic resonance imaging (dMRI) provides a unique probe to examine microscopic structures of the tissues in vivo, but current dMRI techniques usually ignore the spatiotemporal evolution process of the diffusive medium. In the present study, we demonstrate the feasibility to reveal the spatiotemporal diffusion process inside the human brain based on a numerical solution of the diffusion equation. Normal human subjects were scanned with a diffusion tensor imaging (DTI) technique on a 3-T MRI scanner, and the diffusion tensor in each voxel was calculated from the DTI data. The diffusion equation, a partial-derivative description of Fick's law for the diffusion process, was discretized into equivalent algebraic equations. A finite-difference method was employed to obtain the numerical solution of the diffusion equation with a Crank-Nicholson iteration scheme to enhance the numerical stability. By specifying boundary and initial conditions, the spatiotemporal evolution of the diffusion process inside the brain can be virtually reconstructed. Our results exhibit similar medium profiles and diffusion coefficients as those of light fluorescence dextrans measured in integrative optical imaging experiments. The proposed method highlights the feasibility to noninvasively estimate the macroscopic diffusive transport time for a molecule in a given region of the brain.

Salience measure for assessing scale-based features in mammograms.

This work assesses the usefulness of an objective, task-based image quality measure that is correlated with perceived image quality; the measure uses the most salient features contained within a medical image. Contributions include the development of a perceptually correlated metric that is useful for quantifying the salience of local, low-level visual cues and identifying those spatial frequencies that are most distinct and perhaps most relied upon by radiologists for decision making. A set of 40 mammograms and registered eye position data from nine observers was used to evaluate the salience metric. A parsimonious analysis-of-variance model explained the variance in the salience results. This analysis is generalized to a population of readers and cases. An analysis of salience versus time of first eye fixation shows good correlation with true positive lesions that were found by experienced readers in less than 2 s.

Assessing classifiers from two independent data sets using ROC analysis: a nonparametric approach.

This paper considers binary classification. We assess a classifier in terms of the Area Under the ROC Curve (AUC). We estimate three important parameters, the conditional AUC (conditional on a particular training set) and the mean and variance of this AUC. We derive, as well, a closed form expression of the variance of the estimator of the AUC. This expression exhibits several components of variance that facilitate an understanding for the sources of uncertainty of that estimate. In addition, we estimate this variance, i.e., the variance of the conditional AUC estimator. Our approach is nonparametric and based on general methods from U-statistics; it addresses the case where the data distribution is neither known nor modeled and where there are only two available data sets, the training and testing sets. Finally, we illustrate some simulation results for these estimators.

Design and evaluation of an automatic procedure for detection of large misregistration of medical images.

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.

Computer-aided detection of polyps in a colon phantom: effect of scan orientation, polyp size, collimation, and dose.

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.