Albert Xthona

Solution for Nonuniformities and Spatial Noise in Medical LCD Displays by Using Pixel-Based Correction

Liquid crystal displays (LCD) are rapidly replacing cathode ray tube displays (CRT) for medical imaging. LCD technology has improved significantly in the last few years and has important advantages over CRT. However, there are still some aspects of LCD that raise questions as to the usefulness of liquid crystal displays for very subtle clinical diagnosis such as mammography. One drawback of modern LCD displays is the existence of spatial noise expressed as measurable stationary differences in the behavior of individual pixels. This type of noise can be described as a random stationary image superposed on top of the medical image being displayed. It is obvious that this noise image can make subtle structures invisible or add nonexistent patterns to the medical image. In the first case, subtle abnormalities in the medical image could remain undetected, whereas in the second case, it could result into a false positive. This paper describes a method to characterize the spatial noise present in high-resolution medical displays and a technique to solve the problem. A medical display with built-in compensation for the spatial noise at pixel level was developed and improved image quality is demonstrated.

It Is Hard to See a Needle in a Haystack: Modeling Contrast Masking Effect in a Numerical Observer

Within the framework of a virtual clinical trial for breast imaging, we aim to develop numerical observers that follow the same detection performance trends as those of a typical human observer. In our prior work, we showed that by including spatio-temporal contrast sensitivity function (stCSF) of human visual system (HVS) in a multi-slice channelized Hotelling observer (msCHO), we can correctly predict trends of a typical human observer performance with the viewing parameters of browsing speed, viewing distance and contrast. In this work we further improve our numerical observer by modeling contrast masking. After stCSF, contrast masking is the second most prominent property of HVS and it refers to the fact that the presence of one signal affects the visibility threshold for another signal. Our results indicate that the improved numerical observer better predicts changes in detection performance with background complexity.

Does the choice of display system influence perception and visibility of clinically relevant features in digital pathology images

Digital pathology systems typically consist of a slide scanner, processing software, visualization software, and finally a workstation with display for visualization of the digital slide images. This paper studies whether digital pathology images can look different when presenting them on different display systems, and whether these visual differences can result in different perceived contrast of clinically relevant features. By analyzing a set of four digital pathology images of different subspecialties on three different display systems, it was concluded that pathology images look different when visualized on different display systems. The importance of these visual differences is elucidated when they are located in areas of the digital slide that contain clinically relevant features. Based on a calculation of dE2000 differences between background and clinically relevant features, it was clear that perceived contrast of clinically relevant features is influenced by the choice of display system. Furthermore, it seems that the specific calibration target chosen for the display system has an important effect on the perceived contrast of clinically relevant features. Preliminary results suggest that calibrating to DICOM GSDF calibration performed slightly worse than sRGB, while a new experimental calibration target CSDF performed better than both DICOM GSDF and sRGB. This result is promising as it suggests that further research work could lead to better definition of an optimized calibration target for digital pathology images resulting in a positive effect on clinical performance.

Color standard display function: A proposed extension of DICOM GSDF

PURPOSE Color images are being used more in medical imaging for a broad range of modalities and applications. While in the past, color was mostly used for annotations, today color is also widely being used for diagnostic purposes. Surprisingly enough, there is no agreed upon standard yet that describes how color medical images need to be visualized and how calibration and quality assurance of color medical displays need to be performed. This paper proposes color standard display function (CSDF) which is an extension of the DICOM GSDF standard toward color. CSDF defines how color medical displays need to be calibrated and how QA can be performed to obtain perceptually linear behavior not only for grayscale but also for color. METHODS The proposed CSDF algorithm uses DICOM GSDF calibration as a starting point and subsequently uses a color visual difference metric to redistribute colors in order to obtain perceptual linearity not only for the grayscale behavior but also for the color behavior. A clear calibration and quality assurance algorithm is defined and is validated on a wide range of different display systems. RESULTS A detailed description of the proposed CSDF calibration and quality assurance algorithms is provided. These algorithms have been tested extensively on three types of display systems: consumer displays, professional displays, and medical grade displays. Test results are reported both for the calibration algorithm as well as for the quantitative and visual quality assurance methods. The tests confirm that the described algorithm generates consistent results and is able to increase perceptual linearity for color and grayscale visualization. Moreover the proposed algorithms are working well on a wide range of display systems. CONCLUSIONS CSDF has been proposed as an extension of the DICOM GSDF standard toward color. Calibration and QA algorithms for CSDF have been described in detail. The proposed algorithms have been tested on several types of display systems and the results confirm that CSDF largely increases the perceptual linearity of visualized colors, while at the same time remaining compliant with DICOM GSDF.

Perceptual uniformity of commonly used color spaces

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.

Aging display's effect on interpretation of digital pathology slide

It is our conjecture that the variability of colors in a pathology image effects the interpretation of pathology cases, whether it is diagnostic accuracy, diagnostic confidence, or workflow efficiency. In this paper, digital pathology images are analyzed to quantify the perceived difference in color that occurs due to display aging, in particular a change in the maximum luminance, white point, and color gamut. The digital pathology images studied include diagnostically important features, such as the conspicuity of nuclei. Three different display aging models are applied to images: aging of luminance and chrominance, aging of chrominance only, and a stabilized luminance and chrominance (i.e., no aging). These display models and images are then used to compare conspicuity of nuclei using CIE ΔE2000, a perceptual color difference metric. The effect of display aging using these display models and images is further analyzed through a human reader study designed to quantify the effects from a clinical perspective. Results from our reader study indicate significant impact of aged displays on workflow as well as diagnosis. Comparing original (not aged) images to aged images, aged images were significantly more difficult to read (p-value of 0.0005) and took longer to score (p-value of 0.02). Moreover, luminance and chrominance aging significantly reduced inter-session percent agreement of diagnostic scores (p-value of 0.0418).

WE-D-204-04: Color Standard Display Function (CSDF): A Proposed Extension of DICOM GSDF

Purpose: Use of color images in medical imaging has increased significantly. As of today there is no agreed upon standard on how color information needs to be visualized on medical color displays, resulting into large variability of color appearance and it making consistency and quality assurance a challenge. This paper presents a proposed extension of DICOM GSDF towards color. Methods: An extension of the GSDF (Greyscale Standard Display Function) to color is proposed: “CSDF” (color standard display function). CSDF is based on deltaE2000 and offers a perceptually linear color behavior. CSDF uses GSDF as its neutral grey behavior and as such allows for simultaneous presentation of color and greyscale medical images. Results: Compared to the presentation given at the 2015 AAPM annual meeting, the algorithm has been improved, QA metrics have been added, and more elaborate validation has been done.Extensive tests were performed on a variety of displays with different characteristics. For each display, color behavior was characterized, the calibration was executed and a QA/compliance test was performed before and after calibration to quantify the effect of the calibration. Results confirm that CSDF significantly improves perceptual color linearity and that the algorithm is stable and reproducible. Furthermore, CSDF calibration was applied to a variety of medical color images. Results indicate that because of the improved perceptual linearity, CSDF can increase perceived contrast of clinically relevant features in a variety of color medical images. Conclusion: There is a need for an extension of GSDF towards color visualization in order to guarantee consistency and quality. During the 2014 AAPM annual meeting a first proposal for a CSDF (color standard display function) was made. Now an improved and much more validated version of the proposed CSDF will be presented, as well as QA metrics that can be used for measuring calibration quality. Tom Kimpe, Johan Rostang, Gert Van Hoey and Albert Xthona are employees of Barco Healthcare

Requirements, desired characteristics and architectural proposal for a visualization framework for digital pathology

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.

Quantification of detection probability of microcalcifications at increased display luminance levels

Only 70-80% of breast cancer is detected in the screening environment. Detection of microcalcifications is generally incomplete and limits effectiveness of controlling breast cancer through early detection. Any advantage in detection of microcalcifications would be highly welcome. Anecdotal comments from practicing radiologists suggest that increased luminance provides one way to increase the detection of relevant microcalcifications. This paper aims to study the effect of increased display luminance on the detection probability of microcalcifications.

Virtual clinical trial of lesion detection in digital mammography and digital breast tomosynthesis

We have designed and conducted 35 virtual clinical trials (VCTs) of breast lesion detection in digital mammography (DM) and digital breast tomosynthesis (DBT) using a novel open-source simulation pipeline, OpenVCT. The goal of the VCTs is to test in-silico reports that DBT provides substantial improvements in the detectability of masses, while the detectability of microcalcifications remains comparable to DM. For this test, we generated 12 software breast phantoms (volume 700ml, compressed thickness 6.33cm), varying the number of simulated tissue compartments and their shape. Into each phantom, we inserted multiple lesions located 2cm apart in the plane parallel to detector at the level of the nipple. Simulated ellipsoidal masses (oblate spheroids 7mm in diameter and of various thicknesses) and single calcifications of various size and composition were inserted; a total of 17,640 lesions were simulated for this project. DM and DBT projections of phantoms with and without lesions were synthesized assuming a clinical acquisition geometry. Exposure parameters (mAs and kVp) were selected to match AEC settings. Processed DM images and reconstructed DBT slices were obtained using a commercially available software library. Lesion detection was simulated by channelized Hotelling observers, with 15 LG channels and a spread of 22, using independent sets of 480 image samples (150×150 pixel ROIs) for training and 480 samples for testing. Our VCTs showed an average AUC improvement for DBT vs DM of 0.027 for microcalcifications and 0.103 for masses, in close agreement (within 1%) of clinical data reported in the literature.