Tom Kimpe

Consistency and Standardization of Color in Medical Imaging: a Consensus Report

This article summarizes the consensus reached at the Summit on Color in Medical Imaging held at the Food and Drug Administration (FDA) on May 8–9, 2013, co-sponsored by the FDA and ICC (International Color Consortium). The purpose of the meeting was to gather information on how color is currently handled by medical imaging systems to identify areas where there is a need for improvement, to define objective requirements, and to facilitate consensus development of best practices. Participants were asked to identify areas of concern and unmet needs. This summary documents the topics that were discussed at the meeting and recommendations that were made by the participants. Key areas identified where improvements in color would provide immediate tangible benefits were those of digital microscopy, telemedicine, medical photography (particularly ophthalmic and dental photography), and display calibration. Work in these and other related areas has been started within several professional groups, including the creation of the ICC Medical Imaging Working Group.

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.

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.

Defective Pixels in Medical LCD Displays: Problem Analysis and Fundamental Solution

Over the past few years, traditional CRT displays have gradually been replaced by active matrix LCD displays. Each pixel in an LCD display has its own individual transistor that controls the transmittance of that pixel. Occasionally, these individual transistors will short or malfunction, resulting in a defective pixel that always shows the same brightness. This article shows how defective LCD pixels can interfere with subtle features in medical images. A defective pixel affects a broad area around it therefore possibly reducing the quality of diagnosis specifically for highly demanding applications such as mammography. A specialized image processing algorithm provides an innovative solution making these defects completely invisible and recovers information from the defect so the radiologist perceives the medical image correctly.

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.

Remote rendering solutions using web technologies

Remote rendering is a well-known solution to the issue of running high-performance applications requiring complex visualizations on less capable hardware/software platforms or when client access to the data source for visualization is undesired or prohibitive in terms of required bandwidth. Visualizing the output of these remote rendering applications is typically achieved through native applications or, when considering a browser environment, through plug-ins. In this paper, several solutions are presented that enable deployment of these applications on standard web browsers, even those from the pre-HTML5 era. The focus in this paper is on two specific use case scenarios, taking into account that the proposed solutions are generic enough to be applied to a range of similar applications. The technologies presented cover the entire range of sub-processes contained in a complete remote rendering solution, such as the establishment of interaction feedback channels and delivery of images as part of the rendering pipeline. Depending on factors such as application requirements, developer preferences, feature availability in the web browser or raw performance figures, a custom solution can be composed from the options discussed in this paper. This is illustrated by applying them to the two aforementioned use cases, each with specific requirements and challenges, and benchmarking these example setups in terms of performance. A comparison of advantages and disadvantages is presented to guide developers in applying the technologies under real-life conditions.

Power Consumption and Temperature Distribution in WRGB Active-Matrix OLED Displays

In this paper, the power consumption of a white-red-green-blue (WRGB) active-matrix organic light-emitting device (OLED) display and the resulting temperature distribution across the display are analyzed as a function of the applied image and the luminance of the emitted light. It has been shown previously that temperature directly impacts the picture quality of an OLED display. Luminance, spectral radiance, power and temperature measurements are performed on a 55-in WRGB OLED display with a resolution of 1920 ×1080. A power model is presented that allows calculating the displays power consumption for a given applied image. This involves the dependency of the efficiency of the white OLED on the current density, the wavelength dependent transmission of the color filters and the contribution of each of the subpixels in producing the displays nominal white. The output of the power model is used as input for a basic thermal model that simulates the temperature distribution across the display. The thermal model is based on 3D computational fluid dynamics analysis framework (FloEFD). A good agreement between the simulations and measurements on the sample WRGB OLED display is obtained.

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.

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.

On anthropomorphic decision making in a model observer

By analyzing human readers’ performance in detecting small round lesions in simulated digital breast tomosynthesis background in a location known exactly scenario, we have developed a model observer that is a better predictor of human performance with different levels of background complexity (i.e., anatomical and quantum noise). Our analysis indicates that human observers perform a lesion detection task by combining a number of sub-decisions, each an indicator of the presence of a lesion in the image stack. This is in contrast to a channelized Hotelling observer, where the detection task is conducted holistically by thresholding a single decision variable, made from an optimally weighted linear combination of channels. However, it seems that the sub-par performance of human readers compared to the CHO cannot be fully explained by their reliance on sub-decisions, or perhaps we do not consider a sufficient number of subdecisions. To bridge the gap between the performances of human readers and the model observer based upon subdecisions, we use an additive noise model, the power of which is modulated with the level of background complexity. The proposed model observer better predicts the fast drop in human detection performance with background complexity.