Signal to noise ratio quantifies the contribution of spectral channels to classification of human head and neck tissues ex vivo using deep learning and multispectral imaging.

Significance: Accurate identification of tissues is critical for performing safe surgery. Combining multispectral imaging (MSI) with deep learning is a promising approach to increasing tissue discrimination and classification. Evaluating the contributions of spectral channels to tissue discrimination is important for improving MSI systems. Aim: Develop a metric to quantify the contributions of individual spectral channels to tissue classification in MSI. Approach: MSI was integrated into a digital operating microscope with three sensors and seven illuminants. Two convolutional neural network (CNN) models were trained to classify 11 head and neck tissue types using white light (RGB) or MSI images. The signal to noise ratio (SNR) of spectral channels was compared with the impact of channels on tissue classification performance as determined using CNN visualization methods. Results: Overall tissue classification accuracy was higher with use of MSI images compared with RGB images, both for classification of all 11 tissue types and binary classification of nerve and parotid ( p < 0.001 ). Removing spectral channels with SNR > 20 reduced tissue classification accuracy. Conclusions: The spectral channel SNR is a useful metric for both understanding CNN tissue classification and quantifying the contributions of different spectral channels in an MSI system.

Assessment of OLED displays for vision research.

Vision researchers rely on visual display technology for the presentation of stimuli to human and nonhuman observers. Verifying that the desired and displayed visual patterns match along dimensions such as luminance, spectrum, and spatial and temporal frequency is an essential part of developing controlled experiments. With cathode-ray tubes (CRTs) becoming virtually unavailable on the commercial market, it is useful to determine the characteristics of newly available displays based on organic light emitting diode (OLED) panels to determine how well they may serve to produce visual stimuli. This report describes a series of measurements summarizing the properties of images displayed on two commercially available OLED displays: the Sony Trimaster EL BVM-F250 and PVM-2541. The results show that the OLED displays have large contrast ratios, wide color gamuts, and precise, well-behaved temporal responses. Correct adjustment of the settings on both models produced luminance nonlinearities that were well predicted by a power function ("gamma correction"). Both displays have adjustable pixel independence and can be set to have little to no spatial pixel interactions. OLED displays appear to be a suitable, or even preferable, option for many vision research applications.

Digital camera simulation.

We describe a simulation of the complete image processing pipeline of a digital camera, beginning with a radiometric description of the scene captured by the camera and ending with a radiometric description of the image rendered on a display. We show that there is a good correspondence between measured and simulated sensor performance. Through the use of simulation, we can quantify the effects of individual digital camera components on system performance and image quality. This computational approach can be helpful for both camera design and image quality assessment.

Imaging systems and applications.

Imaging systems are used in consumer, medical, and military applications. Designing, developing, and building imaging systems requires a multidisciplinary approach. This issue features current research in imaging systems that ranges from fundamental theories to novel applications. Although the papers collected are diverse, their unique compilation provides a systems perspective to imaging.