KEYWORDS: Breast, X-rays, X-ray imaging, 3D displays, 3D image processing, 3D acquisition, Mammography, Image resolution, Principal component analysis, Imaging systems
Three-dimensional (3-D) imaging has been intensively studied in the past few decades. Depth information is an important added value of 3-D systems over two-dimensional systems. Special focuses were devoted to the development of stereo matching methods for the generation of disparity maps (i.e., depth information within a 3-D scene). Dedicated frameworks were designed to evaluate and rank the performance of different stereo matching methods but never considering x-ray medical images. Yet, 3-D x-ray acquisition systems and 3-D medical displays have already been introduced into the diagnostic market. To access the depth information within x-ray stereoscopic images, computing accurate disparity maps is essential. We aimed at developing a framework dedicated to x-ray stereoscopic breast images used to evaluate and rank several stereo matching methods. A multiresolution pyramid optimization approach was integrated to the framework to increase the accuracy and the efficiency of the stereo matching techniques. Finally, a metric was designed to score the results of the stereo matching compared with the ground truth. Eight methods were evaluated and four of them [locally scaled sum of absolute differences (LSAD), zero mean sum of absolute differences, zero mean sum of squared differences, and locally scaled mean sum of squared differences] appeared to perform equally good with an average error score of 0.04 (0 is the perfect matching). LSAD was selected for generating the disparity maps.
Use of color images in medical imaging has increased significantly the last few years. Color information is essential for applications such as ophthalmology, dermatology and clinical photography. Use of color at least brings benefits for other applications such as endoscopy, laparoscopy and digital pathology. Remarkably, as of today, there is no agreed standard on how color information needs to be visualized for medical applications. This lack of standardization results in large variability of how color images are visualized and it makes quality assurance a challenge. For this reason FDA and ICC recently organized a joint summit on color in medical imaging (CMI). At this summit, one of the suggestions was that modalities such as digital pathology could benefit from using a perceptually uniform color space (T. Kimpe, “Color Behavior of Medical Displays,” CMI presentation, May 2013). Perceptually uniform spaces have already been used for many years in the radiology community where the DICOM GSDF standard provides linearity in luminance but not in color behavior. In this paper we quantify perceptual uniformity, using CIE’s ΔE2000 as a color distance metric, of several color spaces that are typically used for medical applications. We applied our method to theoretical color spaces Gamma 1.8, 2.0, & 2.2, standard sRGB, and DICOM (correction LUT for gray applied to all primaries). In addition, we also measured color spaces (i.e., native behavior) of a high-end medical display (Barco Coronis Fusion 6MP DL, MDCC-6130), and a consumer display (Dell 1907FP). Our results indicate that sRGB & the native color space on the Barco Coronis Fusion exhibit the least non-uniformity within their group. However, the remaining degree of perceptual non-uniformity is still significant and there is room for improvement.
Use of color images in medical imaging has increased significantly the last few years. One of the applications in which color plays an essential role is digital pathology. Remarkably, as of today there is no agreed standard on how color information needs to be processed and visualized for medical imaging applications such as digital pathology. This lack of standardization results into large variability of how color images are visualized and it makes consistency and quality assurance a challenge. For this reason FDA and ICC recently organized a joint summit on color in medical imaging. This paper focuses on the visualization and display side of the digital pathology imaging pipeline. Requirements and desired characteristics for visualization of digital pathology images are discussed in depth. Several technological alternative solutions and considered. And finally a proposal is made for a possible architecture for a display & visualization framework for digital pathology images. The main goal for making this architectural proposal is to facilitate discussion that could lead to standardization.
We investigate improvements to our 3D model observer with the goal of better matching human observer performance as a function of viewing distance, effective contrast, maximum luminance, and browsing speed. Two nonlinear methods of applying the human contrast sensitivity function (CSF) to a 3D model observer are proposed, namely the Probability Map (PM) and Monte Carlo (MC) methods. In the PM method, the visibility probability for each frequency component of the image stack, p, is calculated taking into account Barten’s spatiotemporal CSF, the component modulation, and the human psychometric function. The probability p is considered to be equal to the perceived amplitude of the frequency component and thus can be used by a traditional model observer (e.g., LG-msCHO) in the space-time domain. In the MC method, each component is randomly kept with probability p or discarded with 1-p. The amplitude of the retained components is normalized to unity. The methods were tested using DBT stacks of an anthropomorphic breast phantom processed in a comprehensive simulation pipeline. Our experiments indicate that both the PM and MC methods yield results that match human observer performance better than the linear filtering method as a function of viewing distance, effective contrast, maximum luminance, and browsing speed.
We investigate the effects of common types of image manipulation and image degradation on the perceived image quality (IQ) of digital pathology slides. The reference images in our study were digital images of animal pathology samples (gastric fundic glands of a dog and liver of a foal) stained with haematoxylin and eosin. The following 5 types of artificial manipulations were applied to the images, each very subtle (though visually discernible) and always one at a time: blurring, gamma modification, adding noise, change in color saturation, and JPG compression. Three groups of subjects: pathology experts (PE), pathology students (PS) and imaging experts (IE), assessed 6 IQ attributes in 72 single-stimulus trials. The following perceptual IQ attribute ratings were collected: overall IQ, blur disturbance, quality of contrast, noise disturbance, and quality of color saturation. Our results indicate that IQ ratings vary quite significantly with expertise, especially, PE and IE tend to judge IQ according to different criteria. In particular, IE seem notably more sensitive to noise than PE who, on the other side, tend to be sensitive to manipulations in color and gamma parameters. It remains an important question for future research to examine the impact of IQ on the diagnostic performance of PE. That should support our present findings in suggesting directions for further development of the numerical IQ metrics for digital pathology data.
User experience with viewing images in pathology is crucial for accurate interpretation and diagnosis. With digital
pathology, images are being read on a display system, and this poses new types of questions: such as what is the
difference in terms of pixelation, refresh lag or obscured features compared to an optical microscope. Is there a resultant change in user performance in terms of speed of slide review, perception of adequacy and quality or in diagnostic confidence? A prior psychophysical study was carried out comparing various display modalities on whole slide imaging (WSI) in pathology at the University of Pittsburgh Medical Center (UPMC) in the USA. This prior study compared professional and non-professional grade display modalities and highlighted the importance of using a medical grade display to view pathological digital images. This study was duplicated in Europe at the Department of Pathology in Erasme Hospital (Université Libre de Bruxelles (ULB)) in an attempt to corroborate these findings. Digital WSI with corresponding glass slides of 58 cases including surgical pathology and cytopathology slides of varying difficulty were employed. Similar non-professional and professional grade display modalities were compared to an optical microscope (Olympus BX51). Displays ranged from a laptop (DELL Latitude D620), to a consumer grade display (DELL E248WFPb), to two professional grade monitors (Eizo CG245W and Barco MDCC-6130). Three pathologists were
selected from the Department of Pathology in Erasme Hospital (ULB) in Belgium to view and interpret the pathological
images on these different displays. The results show that non-professional grade displays (laptop and consumer) have
inferior user experience compared to professional grade monitors and the optical microscope.
KEYWORDS: Visual process modeling, Contrast sensitivity, Digital breast tomosynthesis, 3D modeling, Computer simulations, Spatial frequencies, 3D image processing, Breast, Visual system, Medical imaging
Barten’s model of spatio-temporal contrast sensitivity function of human visual system is embedded in a multi-slice channelized Hotelling observer. This is done by 3D filtering of the stack of images with the spatio-temporal contrast sensitivity function and feeding the result (i.e., the perceived image stack) to the multi-slice channelized Hotelling observer. The proposed procedure of considering spatio-temporal contrast sensitivity function is generic in the sense that it can be used with observers other than multi-slice channelized Hotelling observer. Detection performance of the new observer in digital breast tomosynthesis is measured in a variety of browsing speeds, at two spatial sampling rates, using computer simulations. Our results show a peak in detection performance in mid browsing speeds. We compare our results to those of a human observer study reported earlier (I. Diaz et al. SPIE MI 2011). The effects of display luminance, contrast and spatial sampling rate, with and without considering foveal vision, are also studied. Reported simulations are conducted with real digital breast tomosynthesis image stacks, as well as stacks from an anthropomorphic software breast phantom (P. Bakic et al. Med Phys. 2011). Lesion cases are simulated by inserting single micro-calcifications or masses. Limitations of our methods and ways to improve them are discussed.
The aim of this study is to predict the clinical performance and image quality of a display system for viewing dental
images. At present, the use of dedicated medical displays is not uniform among dentists - many still view images on
ordinary consumer displays. This work investigated whether the use of a medical display improved the perception of
dental images by a clinician, compared to a consumer display. Display systems were simulated using the MEdical
Virtual Imaging Chain (MEVIC). Images derived from two carefully performed studies on periodontal bone lesion
detection and endodontic file length determination, were used. Three displays were selected: a medical grade one and
two consumer displays (Barco MDRC-2120, Dell 1907FP and Dell 2007FPb). Some typical characteristics of the
displays are evaluated by measurements and simulations like the Modulation Function (MTF), the Noise Power
Spectrum (NPS), backlight stability or calibration. For the MTF, the display with the largest pixel pitch has logically the
worst MTF. Moreover, the medical grade display has a slightly better MTF and the displays have similar NPS. The study
shows the instability effect for the emitted intensity of the consumer displays compared to the medical grade one. Finally
the study on the calibration methodology of the display shows that the signal in the dental images will be always more
perceivable on the DICOM GSDF display than a gamma 2,2 display.
KEYWORDS: Digital breast tomosynthesis, 3D image processing, Breast cancer, Clinical trials, Breast, 3D acquisition, X-rays, Tissues, Data acquisition, Digital mammography
Digital breast tomosynthesis (DBT) is a new volumetric breast cancer screening modality. It is based on the principles of
computed tomography (CT) and shows promise for improving sensitivity and specificity compared to digital
mammography, which is the current standard protocol. A barrier to critically evaluating any new modality, including
DBT, is the lack of patient data from which statistically significant conclusions can be drawn; such studies require large
numbers of images from both diseased and healthy patients. Since the number of detected lesions is low in relation to the
entire breast cancer screening population, there is a particular need to acquire or otherwise create diseased patient data.
To meet this challenge, we propose a method to insert 3D lesions in the DBT images of healthy patients, such that the
resulting images appear qualitatively faithful to the modality and could be used in future clinical trials or virtual clinical
trials (VCTs). The method facilitates direct control of lesion placement and lesion-to-background contrast and is
agnostic to the DBT reconstruction algorithm employed.
Clinical studies for the validation of new medical imaging devices require hundreds of images. An important step in
creating and tuning the study protocol is the classification of images into "difficult" and "easy" cases. This consists of
classifying the image based on features like the complexity of the background, the visibility of the disease (lesions).
Therefore, an automatic medical background classification tool for mammograms would help for such clinical studies.
This classification tool is based on a multi-content analysis framework (MCA) which was firstly developed to recognize
image content of computer screen shots. With the implementation of new texture features and a defined breast density
scale, the MCA framework is able to automatically classify digital mammograms with a satisfying accuracy. BI-RADS
(Breast Imaging Reporting Data System) density scale is used for grouping the mammograms, which standardizes the
mammography reporting terminology and assessment and recommendation categories. Selected features are input into a
decision tree classification scheme in MCA framework, which is the so called "weak classifier" (any classifier with a
global error rate below 50%). With the AdaBoost iteration algorithm, these "weak classifiers" are combined into a
"strong classifier" (a classifier with a low global error rate) for classifying one category. The results of classification for
one "strong classifier" show the good accuracy with the high true positive rates. For the four categories the results are:
TP=90.38%, TN=67.88%, FP=32.12% and FN =9.62%.
KEYWORDS: Digital breast tomosynthesis, Mammography, Signal detection, Statistical analysis, Breast, Scattering, Imaging systems, Digital mammography, Digital imaging, Error analysis
The main objective of this study is to evaluate and validate the new Barco medical display MDMG-5221 which has been
optimized for the Digital Breast Tomosynthesis (DBT) imaging modality system, and to prove the benefit of the new
DBT display in terms of image quality and clinical performance. The clinical performance is evaluated by the detection
of micro-calcifications inserted in reconstructed Digital Breast Tomosynthesis slices. The slices are shown in dynamic
cine loops, at two frames rates. The statistical analysis chosen for this study is the Receiver Operating Characteristic
Multiple-Reader, Multiple-Case methodology, in order to measure the clinical performance of the two displays. Four
experienced radiologists are involved in this study. For this clinical study, 50 normal and 50 abnormal independent
datasets were used. The result is that the new display outperforms the mammography display for a signal detection task
using real DBT images viewed at 25 and 50 slices per second. In the case of 50 slices per second, the p-value = 0.0664.
For a cut-off where alpha=0.05, the conclusion is that the null hypothesis cannot be rejected, however the trend is that
the new display performs 6% better than the old display in terms of AUC. At 25 slices per second, the difference
between the two displays is very apparent. The new display outperforms the mammography display by 10% in terms of
AUC, with a good statistical significance of p=0.0415.
KEYWORDS: LCDs, Medical imaging, Digital breast tomosynthesis, Signal detection, Image quality, Breast, Data modeling, 3D image processing, Digital imaging, Databases
High performance of the radiologists in the task of image lesion detection is crucial for successful medical practice.
One relevant factor in clinical image reading is the quality of the medical display. With the current trends of
stack-mode liquid crystal displays (LCDs), the slow temporal response of the display plays a significant role in
image quality assurance. In this paper, we report on the experimental study performed to evaluate the quality
of a novel LCD with advanced temporal response compensation, and compare it to an existing state-of-the-art
display of the same category but with no temporal response compensation. The data in the study comprise
clinical digital tomosynthesis images of the breast with added simulated mass lesions. The detectability for the
two displays is estimated using the recent multi-slice channelized Hotelling observer (msCHO) model which is
especially designed for multi-slice image data. Our results suggest that the novel LCD allows higher detectability
than the existing one. Moreover, the msCHO results are used to advise on the parameters for the follow up
image reading study with real medical doctors as observers. Finally, the main findings of the msCHO study were
confirmed by a human reader study (details to be published in a separate paper).
MTF (Modular Transfer Function) is used as a metric for the sharpness of the images displayed on a monitor. However,
MTF is often only measured in one dimension (usually horizontally or vertically). The goal of this work is to provide a
methodology that allows measuring the MTF of a display correctly in 2D. Existing methodologies are analyzed to
determine if they are satisfactory. We concluded that all of the currently used methodologies have shortcomings. To
overcome the limitations of the existing technologies, a new methodology is introduced, the Pixel Spread Function. In
this methodology, an impulse to the display is approximated by a single lit pixel, on a uniform background. The
measurements are performed at a very close distance, to limit the degradations introduced by the measurement system.
By applying signal processing, the 2D MTF has been obtained for this methodology. After averaging the results for
multiple images and pixel positions and removing the camera degradation, the methodology proved to be reproducible
and easy to perform. To validate the proposed methodology, a mathematical model has been created that allows
simulating the MTF. Since the geometrical structure of the pixel has the largest impact on the obtained result, the model
is based on it. Other degradations in the system (aberrations, diffraction...) are approximated by an additional blurring
step. The theoretical and experimental results were compared, and it was concluded that the methodology is valid.
During the European Cantata project (ITEA project, 2006-2009), a Multi-Content Analysis framework for the
classification of compound images in various categories (text, graphical user interface, medical images, other complex
images) was developed within Barco. The framework consists of six parts: a dataset, a feature selection method, a
machine learning based Multi-Content Analysis (MCA) algorithm, a Ground Truth, an evaluation module based on
metrics and a presentation module. This methodology was built on a cascade of decision tree-based classifiers combined
and trained with the AdaBoost meta-algorithm. In order to be able to train these classifiers on large training datasets
without excessively increasing the training time, various optimizations were implemented. These optimizations were
performed at two levels: the methodology itself (feature selection / elimination, dataset pre-computation) and the
decision-tree training algorithm (binary threshold search, dataset presorting and alternate splitting algorithm). These
optimizations have little or no negative impact on the classification performance of the resulting classifiers. As a result,
the training time of the classifiers was significantly reduced, mainly because the optimized decision-tree training
algorithm has a lower algorithmic complexity. The time saved through this optimized methodology was used to compare
the results of a greater number of different training parameters.
KEYWORDS: Image compression, Medical imaging, Image fusion, Quantization, Image quality, Chemical elements, RGB color model, Video, Matrices, Standards development
In medical networked applications, the server-generated application view, consisting of medical image content and
synthetic text/GUI elements, must be compressed and transmitted to the client. To adapt to the local content
characteristics, the application view is divided into rectangular patches, which are classified into content classes: medical
image patches, synthetic image patches consisting of text on a uniform/natural/medical image background and synthetic
image patches consisting of GUI elements on a uniform/natural/medical image background. Each patch is thereafter
compressed using a technique yielding perceptually optimal performance for the identified content class. The goal of this
paper is to identify this optimal technique, given a set of candidate schemes. For this purpose, a simulation framework is
used which simulates different types of compression and measures the perceived differences between the compressed
and original images, taking into account the display characteristics. In a first experiment, JPEG is used to code all
patches and the optimal chroma subsampling and quantization parameters are derived for different content classes. The
results show that 4:4:4 chroma subsampling is the best choice, regardless of the content type. Furthermore, frequency
dependant quantization yields better compression performance than uniform quantization, except for content containing a
significant number of very sharp edges. In a second experiment, each patch can be coded using JPEG, JPEG XR or JPEG
2000. On average, JPEG 2000 outperforms JPEG and JPEG XR for most medical images and for patches containing text.
However, for histopathology or tissue patches and for patches containing GUI elements, classical JPEG compression
outperforms the other two techniques.
Medical-imaging systems are designed to aid medical specialists in a specific task. Therefore, the physical parameters of
a system need to optimize the task performance of a human observer. This requires measurements of human performance
in a given task during the system optimization. Typically, psychophysical studies are conducted for this purpose.
Numerical observer models have been successfully used to predict human performance in several detection tasks.
Especially, the task of signal detection using a channelized Hotelling observer (CHO) in simulated images has been
widely explored. However, there are few studies done for clinically acquired images that also contain anatomic noise.
In this paper, we investigate the performance of a CHO in the task of detecting lung nodules in real radiographic images
of the chest. To evaluate variability introduced by the limited available data, we employ a commonly used study of a
multi-reader multi-case (MRMC) scenario. It accounts for both case and reader variability. Finally, we use the "oneshot"
methods to estimate the MRMC variance of the area under the ROC curve (AUC).
The obtained AUC compares well to those reported for human observer study on a similar data set. Furthermore, the
"one-shot" analysis implies a fairly consistent performance of the CHO with the variance of AUC below 0.002. This
indicates promising potential for numerical observers in optimization of medical imaging displays and encourages
further investigation on the subject.
In the context of the European Cantata project (ITEA project, 2006-2009), within Barco, a complete Multi-Content Analysis framework was developed for detection and analysis of compound images. The framework consists of: a dataset, a Multi-Content Analysis (MCA) algorithm based on learning approaches, a Ground Truth, an evaluation module based on metrics and a presentation module. The aim of the MCA methodology presented here is to classify image content of computer screenshots into different categories such as: text; Graphical User Interface; Medical images and other complex images. The AdaBoost meta-algorithm was chosen, implemented and optimized for the classification method as it fitted the constraints (real-time and precision). A large dataset separated in training and testing subsets and their ground truth (with ViPER metadata format) was both collected and generated for the four different categories. The outcome of the MCA is a cascade of strong classifiers trained and tested on the different subsets. The obtained framework and its optimization (binary search, pre-computing of the features, pre-sorting) allow to re-train the classifiers as much as needed. The preliminary results are quite encouraging with a low false positive rate and close true positive rate in comparison with expectations. The re-injection of false negative examples from new testing subsets in the training phase resulted in better performances of the MCA.
LCDs suffer from viewing angle dependency, meaning that characteristics of LCDs change with viewing angle.
DICOM GSDF calibration and corresponding quality checks typically take place for on-axis viewing. However, users
will use the display for a broad range of viewing angles. Several studies have shown that when calibration is done for
on-axis viewing then the display is not accurately complying with the DICOM GSDF standard when viewing off-axis.
This paper presents a novel solution: we adapt the DICOM GSDF calibration algorithm to have inherent robustness
against change of viewing angle. A validation has been done by means of a 5 Mega Pixel medical display. Results show
that it is possible to double the range of viewing angles (18° instead of 9°) for which the display is within the 10%
tolerance as defined in the DICOM GSDF standard. This result is very useful because users typically will use their
displays also for off-axis viewing angles.
In the context of medical display validation, a simulation chain has been developed to facilitate display design and
image quality validation. One important part is the human visual observer model to quantify the quality perception of
the simulated images. Since several years, multiple research groups are modeling the various aspects of human
perception to integrate them in a complete Human Visual System (HVS) and developing visible image difference
metrics. In our framework, the JNDmetrix is used. It reflects the human subjective assessment of images or video
fidelity. Nevertheless, the system is limited and not suitable for our accurate simulations. There is a limitation to RGB 8
bits integer images and the model takes into account display parameters like gamma, black offset, ambient light... It
needs to be extended. The solutions proposed to extend the HVS model are: precision enhancement to overcome the 8
bit limit, color space conversion between XYZ and RGB and adaptation to the display parameters. The preprocessing
does not introduce any kind of perceived distortion caused for example by precision enhancement. With this extension
the model is used in a daily basis in the display simulation chain.
This paper describes a virtual image chain for medical display (project VICTOR: granted in the 5th framework program by European commission). The chain starts from raw data of an image digitizer (CR, DR) or synthetic patterns and covers image enhancement (MUSICA by Agfa) and both display possibilities, hardcopy (film on viewing box) and softcopy (monitor). Key feature of the chain is a complete image wise approach. A first prototype is implemented in an object-oriented software platform. The display chain consists of several modules. Raw images are either taken from scanners (CR-DR) or from a pattern generator, in which characteristics of DR- CR systems are introduced by their MTF and their dose-dependent Poisson noise. The image undergoes image enhancement and comes to display. For soft display, color and monochrome monitors are used in the simulation. The image is down-sampled. The non-linear response of a color monitor is taken into account by the GOG or S-curve model, whereas the Standard Gray-Scale-Display-Function (DICOM) is used for monochrome display. The MTF of the monitor is applied on the image in intensity levels. For hardcopy display, the combination of film, printer, lightbox and viewing condition is modeled. The image is up-sampled and the DICOM-GSDF or a Kanamori Look-Up-Table is applied. An anisotropic model for the MTF of the printer is applied on the image in intensity levels. The density-dependent color (XYZ) of the hardcopy film is introduced by Look-Up-tables. Finally a Human Visual System Model is applied to the intensity images (XYZ in terms of cd/m2) in order to eliminate nonvisible differences. Comparison leads to visible differences, which are quantified by higher order image quality metrics. A specific image viewer is used for the visualization of the intensity image and the visual difference maps.
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