Effect of Disease Severity and Optic Disc Size on Diagnostic Accuracy of RTVue Spectral Domain Optical Coherence Tomograph in Glaucoma

PURPOSE. To evaluate the effect of disease severity and optic disc size on the diagnostic accuracies of optic nerve head (ONH), retinal nerve fiber layer (RNFL), and macular parameters with RTVue (Optovue, Fremont, CA) spectral domain optical coherence tomography (SDOCT) in glaucoma. METHODS. 110 eyes of 62 normal subjects and 193 eyes of 136 glaucoma patients from the Diagnostic Innovations in Glaucoma Study underwent ONH, RNFL, and macular imaging with RTVue. Severity of glaucoma was based on visual field index (VFI) values from standard automated perimetry. Optic disc size was based on disc area measurement using the Heidelberg Retina Tomograph II (Heidelberg Engineering, Dossenheim, Germany). Influence of disease severity and disc size on the diagnostic accuracy of RTVue was evaluated by receiver operating characteristic (ROC) and logistic regression models. RESULTS. Areas under ROC curve (AUC) of all scanning areas increased (P < 0.05) as disease severity increased. For a VFI value of 99%, indicating early damage, AUCs for rim area, average RNLI thickness, and ganglion cell complex-root mean square were 0.693, 0.799, and 0.779, respectively. For a VFI of 70%, indicating severe damage, corresponding AUCs were 0.828, 0.985, and 0.992, respectively. Optic disc size did not influence the AUCs of any of the SDOCT scanning protocols of RTVue (P > 0.05). Sensitivity of the rim area increased and specificity decreased in large optic discs. CONCLUSIONS. Diagnostic accuracies of RTVue scanning protocols for glaucoma were significantly influenced by disease severity. Sensitivity of the rim area increased in large optic discs at the expense of specificity. (Invest Ophthalmol Vis Sci. 2011;92:1290-1296) DOI:10.1167/iovs.10-5516

Comparison of Different Spectral Domain Optical Coherence Tomography Scanning Areas for Glaucoma Diagnosis

Purpose: To evaluate retinal nerve fiber layer (RNFL), optic nerve head (ONH), and macular thickness measurements for glaucoma detection using the RTVue spectral domain optical coherence tomograph. Design: Diagnostic, case-control study. Participants: One hundred forty eyes of 106 glaucoma patients and 74 eyes of 40 healthy subjects from the Diagnostic Innovations in Glaucoma Study (DIGS). Methods: All patients underwent ocular imaging with the commercially available RTVue. Optic nerve head, RNFL thickness, and macular thickness scans were obtained during the same visit. Receiver operating characteristic (ROC) curves and sensitivities at fixed specificities (80% and 95%) were calculated for each parameter. Main Outcome Measures: Areas under the ROC curves (AUC) and sensitivities at fixed specificities of 80% and 95%. Results: The AUC for the RNFL parameter with best performance, inferior quadrant thickness, was significantly higher than that of the best-performing ONH parameter, inferior rim area (0.884 vs 0.812, respectively; P = 0.04). There was no difference between ROC curve areas of the best RNFL thickness parameters and the best inner macular thickness measurement, ganglion cell complex root mean square (ROC curve area = 0.870). Conclusions: The RTVue RNFL and inner retinal macular thickness measurements had good ability to detect eyes with glaucomatous visual field loss and performed significantly better than ONH parameters.

Effect of Disease Severity on the Performance of Cirrus Spectral-Domain OCT for Glaucoma Diagnosis

PURPOSE. To evaluate the effect of disease severity on the diagnostic accuracy of the Cirrus Optical Coherence Tomograph (Cirrus HD-OCT; Carl Zeiss Meditec, Inc., Dublin, CA) for glaucoma detection. METHODS. One hundred thirty-five glaucomatous eyes of 99 patients and 79 normal eyes of 47 control subjects were recruited from the longitudinal Diagnostic Innovations in Glaucoma Study (DIGS). The severity of the disease was graded based on the visual field index (VFI) from standard automated perimetry. Imaging of the retinal nerve fiber layer (RNFL) was obtained using the optic disc cube protocol available on the Cirrus HD-OCT. Pooled receiver operating characteristic (ROC) curves were initially obtained for each parameter of the Cirrus HD-OCT. The effect of disease severity on diagnostic performance was evaluated by fitting an ROC regression model, with VFI used as a covariate, and calculating the area under the ROC curve (AUCs) for different levels of disease severity. RESULTS. The largest pooled AUCs were for average thickness (0.892), inferior quadrant thickness (0.881), and superior quadrant thickness (0.874). Disease severity had a significant influence on the detection of glaucoma. For the average RNFL thickness parameter, AUCs were 0.962, 0.932, 0.886, and 0.822 for VFIs of 70%, 80%, 90%, and 100%, respectively. CONCLUSIONS. Disease severity had a significant effect on the diagnostic performance of the Cirrus HD-OCT and thus should be considered when interpreting results from this device and when considering the potential applications of this instrument for diagnosing glaucoma in the various clinical settings. (Invest Ophthalmol Vis Sci. 2010;51:4104-4109) DOI:10.1167/iovs.094716

Using a cartographic modeling language to manipulate spectral satellite imagery

Land related information about the Earth's surface is commonIJ found in two forms: (1) map infornlation and (2) satellite image da ta. Satellite imagery provides a good visual picture of what is on the ground but complex image processing is required to interpret features in an image scene. Increasingly, methods are being sought to integrate the knowledge embodied in mop information into the interpretation task, or, alternatively, to bypass interpretation and perform biophysical modeling directly on derived data sources. A cartographic modeling language, as a generic map analysis package, is suggested as a means to integrate geographical knowledge and imagery in a process-oriented view of the Earth. Specialized cartographic models may be developed by users, which incorporate mapping information in performing land classification. In addition, a cartographic modeling language may be enhanced with operators suited to processing remotely sensed imagery. We demonstrate the usefulness of a cartographic modeling language for pre-processing satellite imagery, and define two nerv cartographic operators that evaluate image neighborhoods as post-processing operations to interpret thematic map values. The language and operators are demonstrated with an example image classification task.

Stacked pin Devices for Imaging Applications

In this paper we present results on the optimization of device architectures for colour and imaging applications, using a device with a TCO/pinpi'n/TCO configuration. The effect of the applied voltage on the color selectivity is discussed. Results show that the spectral response curves demonstrate rather good separation between the red, green and blue basic colors. Combining the information obtained under positive and negative applied bias a colour image is acquired without colour filters or pixel architecture. A low level image processing algorithm is used for the colour image reconstruction.

The Impact of Multifocal Intraocular Lens in Retinal Imaging with Optical Coherence Tomography

Multifocal intraocular lenses (MF IOLs) have concentric optical zones with different dioptric power, enabling patients to have good visual acuity at multiple focal points. However, several optical limitations have been attributed to this particular design. The purpose of this study is to access the effect of MF IOLs design on the accuracy of retinal optical coherence tomography (OCT). Cross-sectional study conducted at the Refractive Surgery Department of Central Lisbon Hospital Center. Twenty-three eyes of 15 patients with a diffractive MF IOL and 27 eyes of 15 patients with an aspheric monofocal IOL were included in this study. All patients underwent OCT macular scans using Heidelberg Spectralis®. Macular thickness and volume values and image quality (Q factor) were compared between the two groups. There were no statistically significant differences between both groups regarding macular thickness or volume measurements. Retinal OCT image quality was significantly lower in the MF IOL group (p < 0.01). MF IOLs are associated with a significant decrease in OCT image quality. However, this fact does not seem to compromise the accuracy of spectral domain OCT retinal measurements.

Research development of terahertz imaging systems

The electromagnetic radiation at a terahertz frequencies (from 0.1 THz to 10 THz) is situated in the frequency band comprised between the optical band and the radio band. The interest of the scientific community in this frequency band has grown up due to its large capabilities to develop innovative imaging systems. The terahertz waves are able to generate extremely short pulses that achieve good spatial resolution, good penetration capabilities and allow to identify microscopic structures using spectral analysis. The work carried out during the period of the grant has been based on the developement of system working at the aforementioned frequency band. The main system is based on a total power radiometer working at 0.1 THz to perform security imaging. Moreover, the development of this system has been useful to gain knowledge in the behavior of the component systems at this frequency band. Moreover, a vectorial network analyzer has been used to characterize materials and perform active raster imaging. A materials measurement system has been designed and used to measure material properties as permittivity, losses and water concentration. Finally, the design of a terahertz time-domain spectrometer (THz-TDS) system has been started. This system will allow to perform tomographic measurement with very high penetration resolutions while allowing the spectral characterization of the sample material. The application range of this kind of system is very wide: from the identification of cancerous tissues of a skin to the characterization of the thickness of a painted surface of a car.

Evaluation of a linear spectral mixture model and vegetation indices (NDVI and EVI) in a study of schistosomiasis mansoni and Biomphalaria glabrata distribution in the state of Minas Gerais, Brazil

This paper analyses the associations between Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) on the prevalence of schistosomiasis and the presence of Biomphalaria glabrata in the state of Minas Gerais (MG), Brazil. Additionally, vegetation, soil and shade fraction images were created using a Linear Spectral Mixture Model (LSMM) from the blue, red and infrared channels of the Moderate Resolution Imaging Spectroradiometer spaceborne sensor and the relationship between these images and the prevalence of schistosomiasis and the presence of B. glabrata was analysed. First, we found a high correlation between the vegetation fraction image and EVI and second, a high correlation between soil fraction image and NDVI. The results also indicate that there was a positive correlation between prevalence and the vegetation fraction image (July 2002), a negative correlation between prevalence and the soil fraction image (July 2002) and a positive correlation between B. glabrata and the shade fraction image (July 2002). This paper demonstrates that the LSMM variables can be used as a substitute for the standard vegetation indices (EVI and NDVI) to determine and delimit risk areas for B. glabrata and schistosomiasis in MG, which can be used to improve the allocation of resources for disease control.

Improvement of the specificity of cancer detection by autofluorescence imaging in the tracheo-bronchial tree using backscattered violet light.

BACKGROUND: Autofluorescence bronchoscopy (AFB) is a highly sensitive tool for the detection of early bronchial cancers. However, its specificity remains limited due to primarily false positive results induced by hyperplasia, metaplasia and inflammation. We have investigated the potential of blue-violet backscattered light to eliminate false positive results during AFB in a clinical pilot study. METHODS: The diagnostic autofluorescence endoscopy (DAFE) system was equipped with a variable band pass filter in the imaging detection path. The backscattering properties of normal and abnormal bronchial mucosae were assessed by computing the contrast between the two tissue types for blue-violet wavelengths ranging between 410 and 490 nm in 12 patients undergoing routine DAFE examination. In a second study including 6 patients we used a variable long pass (LP) filter to determine the spectral design of the emission filter dedicated to the detection of this blue-violet light with the DAFE system. RESULTS: (Pre-)neoplastic mucosa showed a clear wavelength dependence of the backscattering properties of blue-violet light while the reflectivity of normal, metaplastic and hyperplastic autofluorescence positive mucosa was wavelength independent. CONCLUSIONS: Our results showed that the detection of blue-violet light has the potential to reduce the number of false positive results in AFB. In addition we determined the spectral design of the emission filter dedicated to the detection of this blue-violet light with the DAFE system.

Influence of average size and interface passivation on the spectral emission of Si nanocrystals embedded in SiO2

The correlation between the structural (average size and density) and optoelectronic properties [band gap and photoluminescence (PL)] of Si nanocrystals embedded in SiO2 is among the essential factors in understanding their emission mechanism. This correlation has been difficult to establish in the past due to the lack of reliable methods for measuring the size distribution of nanocrystals from electron microscopy, mainly because of the insufficient contrast between Si and SiO2. With this aim, we have recently developed a successful method for imaging Si nanocrystals in SiO2 matrices. This is done by using high-resolution electron microscopy in conjunction with conventional electron microscopy in dark field conditions. Then, by varying the time of annealing in a large time scale we have been able to track the nucleation, pure growth, and ripening stages of the nanocrystal population. The nucleation and pure growth stages are almost completed after a few minutes of annealing time at 1100°C in N2 and afterward the ensemble undergoes an asymptotic ripening process. In contrast, the PL intensity steadily increases and reaches saturation after 3-4 h of annealing at 1100°C. Forming gas postannealing considerably enhances the PL intensity but only for samples annealed previously in less time than that needed for PL saturation. The effects of forming gas are reversible and do not modify the spectral shape of the PL emission. The PL intensity shows at all times an inverse correlation with the amount of Pb paramagnetic centers at the Si-SiO2 nanocrystal-matrix interfaces, which have been measured by electron spin resonance. Consequently, the Pb centers or other centers associated with them are interfacial nonradiative channels for recombination and the emission yield largely depends on the interface passivation. We have correlated as well the average size of the nanocrystals with their optical band gap and PL emission energy. The band gap and emission energy shift to the blue as the nanocrystal size shrinks, in agreement with models based on quantum confinement. As a main result, we have found that the Stokes shift is independent of the average size of nanocrystals and has a constant value of 0.26±0.03 eV, which is almost twice the energy of the Si¿O vibration. This finding suggests that among the possible channels for radiative recombination, the dominant one for Si nanocrystals embedded in SiO2 is a fundamental transition spatially located at the Si¿SiO2 interface with the assistance of a local Si-O vibration.

Breast cancer and melanoma cell line identification by FTIR imaging after formalin-fixation and paraffin-embedding.

Over the past few decades, Fourier transform infrared (FTIR) spectroscopy coupled to microscopy has been recognized as an emerging and potentially powerful tool in cancer research and diagnosis. For this purpose, histological analyses performed by pathologists are mostly carried out on biopsied tissue that undergoes the formalin-fixation and paraffin-embedding (FFPE) procedure. This processing method ensures an optimal and permanent preservation of the samples, making FFPE-archived tissue an extremely valuable source for retrospective studies. Nevertheless, as highlighted by previous studies, this fixation procedure significantly changes the principal constituents of cells, resulting in important effects on their infrared (IR) spectrum. Despite the chemical and spectral influence of FFPE processing, some studies demonstrate that FTIR imaging allows precise identification of the different cell types present in biopsied tissue, indicating that the FFPE process preserves spectral differences between distinct cell types. In this study, we investigated whether this is also the case for closely related cell lines. We analyzed spectra from 8 cancerous epithelial cell lines: 4 breast cancer cell lines and 4 melanoma cell lines. For each cell line, we harvested cells at subconfluence and divided them into two sets. We first tested the "original" capability of FTIR imaging to identify these closely related cell lines on cells just dried on BaF2 slides. We then repeated the test after submitting the cells to the FFPE procedure. Our results show that the IR spectra of FFPE processed cancerous cell lines undergo small but significant changes due to the treatment. The spectral modifications were interpreted as a potential decrease in the phospholipid content and protein denaturation, in line with the scientific literature on the topic. Nevertheless, unsupervised analyses showed that spectral proximities and distances between closely related cell lines were mostly, but not entirely, conserved after FFPE processing. Finally, PLS-DA statistical analyses highlighted that closely related cell lines are still successfully identified and efficiently distinguished by FTIR spectroscopy after FFPE treatment. This last result paves the way towards identification and characterization of cellular subtypes on FFPE tissue sections by FTIR imaging, indicating that this analysis technique could become a potential useful tool in cancer research.

Modelling plant species distribution in alpine grasslands using airborne imaging spectroscopy.

Remote sensing using airborne imaging spectroscopy (AIS) is known to retrieve fundamental optical properties of ecosystems. However, the value of these properties for predicting plant species distribution remains unclear. Here, we assess whether such data can add value to topographic variables for predicting plant distributions in French and Swiss alpine grasslands. We fitted statistical models with high spectral and spatial resolution reflectance data and tested four optical indices sensitive to leaf chlorophyll content, leaf water content and leaf area index. We found moderate added-value of AIS data for predicting alpine plant species distribution. Contrary to expectations, differences between species distribution models (SDMs) were not linked to their local abundance or phylogenetic/functional similarity. Moreover, spectral signatures of species were found to be partly site-specific. We discuss current limits of AIS-based SDMs, highlighting issues of scale and informational content of AIS data.

Evaluating effects of spectral training data distribution on continuous field mapping performance

Continuous field mapping has to address two conflicting remote sensing requirements when collecting training data. On one hand, continuous field mapping trains fractional land cover and thus favours mixed training pixels. On the other hand, the spectral signature has to be preferably distinct and thus favours pure training pixels. The aim of this study was to evaluate the sensitivity of training data distribution along fractional and spectral gradients on the resulting mapping performance. We derived four continuous fields (tree, shrubherb, bare, water) from aerial photographs as response variables and processed corresponding spectral signatures from multitemporal Landsat 5 TM data as explanatory variables. Subsequent controlled experiments along fractional cover gradients were then based on generalised linear models. Resulting fractional and spectral distribution differed between single continuous fields, but could be satisfactorily trained and mapped. Pixels with fractional or without respective cover were much more critical than pure full cover pixels. Error distribution of continuous field models was non-uniform with respect to horizontal and vertical spatial distribution of target fields. We conclude that a sampling for continuous field training data should be based on extent and densities in the fractional and spectral, rather than the real spatial space. Consequently, adequate training plots are most probably not systematically distributed in the real spatial space, but cover the gradient and covariate structure of the fractional and spectral space well. (C) 2009 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil

The objective of this work was to evaluate the application of the spectral-temporal response surface (STRS) classification method on Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) sensor images in order to estimate soybean areas in Mato Grosso state, Brazil. The classification was carried out using the maximum likelihood algorithm (MLA) adapted to the STRS method. Thirty segments of 30x30 km were chosen along the main agricultural regions of Mato Grosso state, using data from the summer season of 2005/2006 (from October to March), and were mapped based on fieldwork data, TM/Landsat-5 and CCD/CBERS-2 images. Five thematic classes were considered: Soybean, Forest, Cerrado, Pasture and Bare Soil. The classification by the STRS method was done over an area intersected with a subset of 30x30-km segments. In regions with soybean predominance, STRS classification overestimated in 21.31% of the reference values. In regions where soybean fields were less prevalent, the classifier overestimated 132.37% in the acreage of the reference. The overall classification accuracy was 80%. MODIS sensor images and the STRS algorithm showed to be promising for the classification of soybean areas in regions with the predominance of large farms. However, the results for fragmented areas and smaller farms were less efficient, overestimating soybean areas.