Cédric Marchessoux

A Virtual Image Chain for Perceived and Clinical Image Quality of Medical Display

When designing a new medical display, decisions, e.g., on the choice of the panel or the back light, must be taken. First decisions are mostly made based on physical measurements and not really on clinical or perceived quality. To prove clinical quality of the display costly time-consuming psycho-physical/clinical tests are performed. To solve these issues, a medical virtual image chain (MEVIC) was developed from image capture part until the visualization for facilitating medical display design. The chain is composed of three main modules: a virtual image part, a virtual medical display and a virtual specialist. The complete chain is described with a main focus on medical display simulation with many possible applications.

Integration of spatio-temporal contrast sensitivity with a multi-slice channelized Hotelling observer

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.

Development and evaluation of a 3D model observer with nonlinear spatiotemporal contrast sensitivity

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.

Psycho-visual evaluation of image quality attributes in digital pathology slides viewed on a medical color LCD display

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.

Validation of a new digital breast tomosynthesis medical display

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.

Performance evaluation of medical LCD displays using 3D channelized Hotelling observers

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).

3D lesion insertion in digital breast tomosynthesis images

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.

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.

Comparison study of five different display modalities for whole slide images in surgical pathology and cytopathology in Europe

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.

Learning approach for multicontent analysis of compound images

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.