Imaging how and where we breathe oxygen: Another Big Short?

The Big Short tells the story of a small group of skeptics who profited from the financial crisis in 2007 by betting against collateralized (mortgage) debt obligations (CDO). Importantly, the novel paints a clear picture of the eccentric nature of contrarians who think divergently and against the grain or bet against an accepted truth or “sure” thing. In a similar manner, Ishii and co-workers’ recent work describes their team’s development of a pulmonary imaging technology that provides divergent and disruptive in vivo lung measurements of oxygen partial pressure in the context of the prevailing and longstanding consensus around FEV1 as the definitive diagnostic of chronic lung disease. The Big Short tells the story of a small group of skeptics who profited from the financial crisis in 2007 by betting against collateralized (mortgage) debt obligations (CDO). Importantly, the novel paints a clear picture of the eccentric nature of contrarians who think divergently and against the grain or bet against an accepted truth or “sure” thing. In a similar manner, Ishii and co-workers’ recent work describes their team’s development of a pulmonary imaging technology that provides divergent and disruptive in vivo lung measurements of oxygen partial pressure in the context of the prevailing and longstanding consensus around FEV1 as the definitive diagnostic of chronic lung disease

Multiresolution wavelet framework models brightness induction effects

A new multiresolution wavelet model is presented here, which accounts for brightness assimilation and contrast effects in a unified framework, and includes known psychophysical and physiological attributes of the primate visual system (such as spatial frequency channels, oriented receptive fields, contrast sensitivity function, contrast non-linearities, and a unified set of parameters). Like other low-level models, such as the ODOG model [Blakeslee, B., & McCourt, M. E. (1999). A multiscale spatial filtering account of the white effect, simultaneous brightness contrast and grating induction. Vision Research, 39, 4361-4377], this formulation reproduces visual effects such as simultaneous contrast, the White effect, grating induction, the Todorović effect, Mach bands, the Chevreul effect and the Adelson-Logvinenko tile effects, but it also reproduces other previously unexplained effects such as the dungeon illusion, all using a single set of parameters

Low-level spatiochromatic grouping for saliency estimation

We propose a saliency model termed SIM (saliency by induction mechanisms), which is based on a low-level spatiochromatic model that has successfully predicted chromatic induction phenomena. In so doing, we hypothesize that the low-level visual mechanisms that enhance or suppress image detail are also responsible for making some image regions more salient. Moreover, SIM adds geometrical grouplets to enhance complex low-level features such as corners, and suppress relatively simpler features such as edges. Since our model has been fitted on psychophysical chromatic induction data, it is largely nonparametric. SIM outperforms state-of-the-art methods in predicting eye fixations on two datasets and using two metrics

Saliency estimation using a non-parametric low-level vision model

Many successful models for predicting attention in a scene involve three main steps: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. However, integrating spatial information and justifying the choice of various parameter values remain open problems. In this paper we show that an efficient model of color appearance in human vision, which contains a principled selection of parameters as well as an innate spatial pooling mechanism, can be generalized to obtain a saliency model that outperforms state-of-the-art models. Scale integration is achieved by an inverse wavelet transform over the set of scale-weighted center-surround responses. The scale-weighting function (termed ECSF) has been optimized to better replicate psychophysical data on color appearance, and the appropriate sizes of the center-surround inhibition windows have been determined by training a Gaussian Mixture Model on eye-fixation data, thus avoiding ad-hoc parameter selection. Additionally, we conclude that the extension of a color appearance model to saliency estimation adds to the evidence for a common low-level visual front-end for different visual tasks

Perception based representations for computational colour

The perceived colour of a stimulus is dependent on multiple factors stemming out either from the context of the stimulus or idiosyncrasies of the observer. The complexity involved in combining these multiple effects is the main reason for the gap between classical calibrated colour spaces from colour science and colour representations used in computer vision, where colour is just one more visual cue immersed in a digital image where surfaces, shadows and illuminants interact seemingly out of control. With the aim to advance a few steps towards bridging this gap we present some results on computational representations of colour for computer vision. They have been developed by introducing perceptual considerations derived from the interaction of the colour of a point with its context. We show some techniques to represent the colour of a point influenced by assimilation and contrast effects due to the image surround and we show some results on how colour saliency can be derived in real images. We outline a model for automatic assignment of colour names to image points directly trained on psychophysical data. We show how colour segments can be perceptually grouped in the image by imposing shading coherence in the colour space

Chronic Obstructive Pulmonary Disease: Thoracic CT Texture Analysis and Machine Learning to Predict Pulmonary Ventilation

Background Fixed airflow limitation and ventilation heterogeneity are common in chronic obstructive pulmonary disease (COPD). Conventional noncontrast CT provides airway and parenchymal measurements but cannot be used to directly determine lung function. Purpose To develop, train, and test a CT texture analysis and machine-learning algorithm to predict lung ventilation heterogeneity in participants with COPD. Materials and Methods In this prospective study

Coloresia : An Interactive Colour Perception Device for the Visually Impaired

A significative percentage of the human population suffer from impairments in their capacity to distinguish or even see colours. For them, everyday tasks like navigating through a train or metro network map becomes demanding. We present a novel technique for extracting colour information from everyday natural stimuli and presenting it to visually impaired users as pleasant, non-invasive sound. This technique was implemented inside a Personal Digital Assistant (PDA) portable device. In this implementation, colour information is extracted from the input image and categorised according to how human observers segment the colour space. This information is subsequently converted into sound and sent to the user via speakers or headphones. In the original implementation, it is possible for the user to send its feedback to reconfigure the system, however several features such as these were not implemented because the current technology is limited.We are confident that the full implementation will be possible in the near future as PDA technology improves

Pulmonary Imaging Biomarkers of Gas Trapping and Emphysema in COPD: (3)He MR Imaging and CT Parametric Response Maps

PURPOSE: To directly compare magnetic resonance (MR) imaging and computed tomography (CT) parametric response map (PRM) measurements of gas trapping and emphysema in ex-smokers both with and without chronic obstructive pulmonary disease (COPD). MATERIALS AND METHODS: Participants provided written informed consent to a protocol that was approved by a local research ethics board and Health Canada and was compliant with the HIPAA (Institutional Review Board Reg. #00000940). The prospectively planned study was performed from March 2014 to December 2014 and included 58 ex-smokers (mean age, 73 years ± 9) with (n = 32; mean age, 74 years ± 7) and without (n = 26; mean age, 70 years ± 11) COPD. MR imaging (at functional residual capacity plus 1 L), CT (at full inspiration and expiration), and spirometry or plethysmography were performed during a 2-hour visit to generate ventilation defect percent (VDP), apparent diffusion coefficient (ADC), and PRM gas trapping and emphysema measurements. The relationships between pulmonary function and imaging measurements were determined with analysis of variance (ANOVA), Holm-Bonferroni corrected Pearson correlations, multivariate regression modeling, and the spatial overlap coefficient (SOC). RESULTS: VDP, ADC, and PRM gas trapping and emphysema (ANOVA, P \u3c .001) measurements were significantly different in healthy ex-smokers than they were in ex-smokers with COPD. In all ex-smokers, VDP was correlated with PRM gas trapping (r = 0.58, P \u3c .001) and with PRM emphysema (r = 0.68, P \u3c .001). VDP was also significantly correlated with PRM in ex-smokers with COPD (gas trapping: r = 0.47 and P = .03; emphysema: r = 0.62 and P \u3c .001) but not in healthy ex-smokers. In a multivariate model that predicted PRM gas trapping, the forced expiratory volume in 1 second normalized to the forced vital capacity (standardized coefficients [βS] = -0.69, P = .001) and airway wall area percent (βS = -0.22, P = .02) were significant predictors. PRM emphysema was predicted by the diffusing capacity for carbon monoxide (βS = -0.29, P = .03) and VDP (βS = 0.41, P = .001). Helium 3 ADC values were significantly elevated in PRM gas-trapping regions (P \u3c .001). The spatial relationship for ventilation defects was significantly greater with PRM gas trapping than with PRM emphysema in patients with mild (for gas trapping, SOC = 36% ± 28; for emphysema, SOC = 1% ± 2; P = .001) and moderate (for gas trapping, SOC = 34% ± 28; for emphysema, SOC = 7% ± 15; P = .006) COPD. For severe COPD, the spatial relationship for ventilation defects with PRM emphysema (SOC = 64% ± 30) was significantly greater than that for PRM gas trapping (SOC = 36% ± 18; P = .01). CONCLUSION: In all ex-smokers, ADC values were significantly elevated in regions of PRM gas trapping, and VDP was quantitatively and spatially related to both PRM gas trapping and PRM emphysema. In patients with mild to moderate COPD, VDP was related to PRM gas trapping, whereas in patients with severe COPD, VDP correlated with both PRM gas trapping and PRM emphysema

Magnetic resonance imaging biomarkers of chronic obstructive pulmonary disease prior to radiation therapy for non-small cell lung cancer.

OBJECTIVE: In this prospectively planned interim-analysis, the prevalence of chronic obstructive lung disease (COPD) phenotypes was determined using magnetic resonance imaging (MRI) and X-ray computed tomography (CT) in non-small-cell-lung-cancer (NSCLC) patients. MATERIALS AND METHODS: Stage-III-NSCLC patients provided written informed consent for pulmonary function tests, imaging and the 6-min-walk-test. Ventilation defect percent (VDP) and CT lung density (relative-of-CT-density-histogram RESULTS: Seventeen stage-III NSCLC patients were evaluated (68 ± 7 years, 7 M/10 F, mean FEV1 = 77%pred) including seven current and 10 ex-smokers and eight patients with a prior lung disease diagnosis. There was a significant difference for smoking history (p = .02) and FEV1/FVC (p = .04) for subgroups classified using quantitative imaging. Patient subgroups classified using qualitative imaging findings were significantly different for emphysema (RA950, p \u3c .001). There were significant relationships for whole-lung VDP (p \u3c .05), but not RECIST or tumour-lobe VDP measurements with pulmonary function and exercise measurements. Preliminary analysis for non-tumour burden ventilation abnormalities using Reader-operator-characteristic (ROC) curves reflected a 94% classification rate for smoking pack-years, 93% for FEV1/FVC and 82% for RA950. ROC sensitivity/specificity/positive/negative likelihood ratios were also generated for pack-years, (0.92/0.80/4.6/0.3), FEV1/FVC (0.92/0.80/4.6/0.3), RA950 (0.92/0.80/4.6/0.3) and RECIST (0.58/0.80/2.9/1.1). CONCLUSIONS: In this prospectively planned interim-analysis of a larger clinical trial, NSCLC patients were classified based on COPD imaging phenotypes. A proof-of-concept evaluation showed that FEV1/FVC and smoking history identified NSCLC patients with ventilation abnormalities appropriate for functional lung avoidance radiotherapy

Pulmonary Imaging Phenotypes of Chronic Obstructive Pulmonary Disease Using Multiparametric Response Maps

Background Pulmonary imaging of chronic obstructive pulmonary disease (COPD) has focused on CT or MRI measurements, but these have not been evaluated in combination. Purpose To generate multiparametric response map (mPRM) measurements in ex-smokers with or without COPD by using volume-matched CT and hyperpolarized helium 3