Multispectral Scanning Research Articles

Spatial proximity between T and PD-L1 expressing cells as a prognostic biomarker for oropharyngeal squamous cell carcinoma

BackgroundFulfilling the promise of cancer immunotherapy requires novel predictive biomarkers to characterise the host immune microenvironment. Deciphering the complexity of immune cell interactions requires an automated multiplex approach to histological analysis of tumour sections. We tested a new automatic approach to select tissue and quantify the frequencies of cell-cell spatial interactions occurring in the PD1/PD-L1 pathway, hypothesised to reflect immune escape in oropharyngeal squamous cell carcinoma (OPSCC).MethodsSingle sections of diagnostic biopsies from 72 OPSCC patients were stained using multiplex immunofluorescence (CD8, PD1, PD-L1, CD68). Following multispectral scanning and automated regions-of-interest selection, the Hypothesised Interaction Distribution (HID) method quantified spatial proximity between cells. Method applicability was tested by investigating the prognostic significance of co-localised cells (within 30 μm) in patients stratified by HPV status.ResultsHigh frequencies of proximal CD8+ and PD-L1+ (HR 2.95, p = 0.025) and PD1+ and PD-L1+ (HR 2.64, p = 0.042) cells were prognostic for poor overall survival in patients with HPV negative OPSCC (n = 31).ConclusionThe HID method can quantify spatial interactions considered to reflect immune escape and generate prognostic information in OPSCC. The new automated approach is ready to test in additional cohorts and its applicability should be explored in research and clinical studies.

Study of effects of electromagnetic factors on the early development of biological organisms using comprehensive multispectral optical and scanning acoustic microscopy approach

The effects of electromagnetic field on development of biological organisms were studied in this work by multispectral optical microscopy and scanning acoustic microscopy. The living embryos of loach Misgurnus fossilis at early stages of development were used as biological objects. The electric field applied to the living organism was generated by a specially designed unit that can operate independently or in combination with the acoustic microscope. It was shown that the sequentially recorded multispectral optical and acoustical images reveal dynamics of the development. The long lasting experiments confirm minor effect of the optical and acoustic radiation on the embryos. The scanning acoustic microscope was used for in vivo visualization of three dimensional structure of the egg, estimation of the acoustical properties of the yolk tissue and visualization of slow substance flows inside the object. The speed of motion depends on the location of the investigated area, status of the organism and its development stage. The estimated valued of the speed is in a range of 0.1 – 1 µm/s. It was shown that the application of the electric field causes deceleration of the embryos development.

Leaf Scanner: A portable and low-cost multispectral corn leaf scanning device for precise phenotyping

During recent years, portable plant phenotyping instruments have become increasingly important to monitor conditions of plant health non-destructively in the greenhouse and field environments. These devices, such as the Soil Plant Analysis Development (SPAD) meter, leverage indices that represent leaf chlorophyll content due to its strong correlation with leaf nitrogen (N) content. However, instruments such as SPAD meters are expensive and measure only individual, restricted leaf regions per data capture. In this publication, we developed a Leaf Scanner device to analyze the chlorophyll content distributions of whole leaves with greater efficiency and precision. The validating samples for this device were top-collared corn leaves grown under high and low N treatments in a greenhouse. For each region of a corn leaf, this device rapidly flashed visible and near infrared (NIR) LEDs to obtain the visible and NIR transmittance images of leaves. These images were summarized pixel-wisely into the Green NDVI index. A sufficient high framerate permitted continuous collection of regional index images. Using image registration techniques, these regional images were stitched together into a ‘leaf panorama’. The total pixel amount of each leaf panorama, thus, served as a substitute for leaf area. A Minolta SPAD-502Plus meter collected ground-truth measurements along the length of each leaf sample. The results showed that there was a strong correlation between the average Leaf Scanner’s measurements and averaged SPAD values (R2 = 0.92), and the Leaf Scanner was able to clearly detect differences between high and low fertilized samples in terms of chlorophyll content and leaf area. Furthermore, this Leaf Scanner device enabled us to study the chlorophyll content distributions on the plants under different treatments.

Early Detection of Invasive Exotic Trees Using UAV and Manned Aircraft Multispectral and LiDAR Data

Exotic conifers can provide significant ecosystem services, but in some environments, they have become invasive and threaten indigenous ecosystems. In New Zealand, this phenomenon is of considerable concern as the area occupied by invasive exotic trees is large and increasing rapidly. Remote sensing methods offer a potential means of identifying and monitoring land infested by these trees, enabling managers to efficiently allocate resources for their control. In this study, we sought to develop methods for remote detection of exotic invasive trees, namely Pinus sylvestris and P. ponderosa. Critically, the study aimed to detect these species prior to the onset of maturity and coning as this is important for preventing further spread. In the study environment in New Zealand’s South Island, these species reach maturity and begin bearing cones at a young age. As such, detection of these smaller individuals requires specialist methods and very high-resolution remote sensing data. We examined the efficacy of classifiers developed using two machine learning algorithms with multispectral and laser scanning data collected from two platforms—manned aircraft and unmanned aerial vehicles (UAV). The study focused on a localized conifer invasion originating from a multi-species pine shelter belt in a grassland environment. This environment provided a useful means of defining the detection thresholds of the methods and technologies employed. An extensive field dataset including over 17,000 trees (height range = 1 cm to 476 cm) was used as an independent validation dataset for the detection methods developed. We found that data from both platforms and using both logistic regression and random forests for classification provided highly accurate (kappa < 0.996 ) detection of invasive conifers. Our analysis showed that the data from both UAV and manned aircraft was useful for detecting trees down to 1 m in height and therefore shorter than 99.3% of the coning individuals in the study dataset. We also explored the relative contribution of both multispectral and airborne laser scanning (ALS) data in the detection of invasive trees through fitting classification models with different combinations of predictors and found that the most useful models included data from both sensors. However, the combination of ALS and multispectral data did not significantly improve classification accuracy. We believe that this was due to the simplistic vegetation and terrain structure in the study site that resulted in uncomplicated separability of invasive conifers from other vegetation. This study provides valuable new knowledge of the efficacy of detecting invasive conifers prior to the onset of coning using high-resolution data from UAV and manned aircraft. This will be an important tool in managing the spread of these important invasive plants.

Abstract 153: Quantifying tumor heterogeneity and mapping complex immune cell interactions with high-throughput, 7-color multispectral slide scans

Abstract Introduction As the need for multiple biomarker assessment in immune-oncology has become more clear, multiplex fluorescent immunohistochemistry (fIHC) techniques have become integral to immune oncology research. Applying multispectral approaches to fIHC improves the quantitative performance by ensuring removal of autofluorescence signals and ensuring signal specificity by removing bleed-through between spectrally-adjacent dyes. We have recently demonstrated a novel high-throughput multispectral scanning approach that allows acquisition of a 7-color multispectral slide scan in 5 - 10 minutes. Here, we demonstrate the spatial measurements that have now been made possible by this high-throughput, translational workflow. These measurements span the scales related to tumor biology from the distance between nearest cell neighbors to the extent of the invasive margin. Methods Formalin-fixed paraffin-embedded samples of primary tumors were immunostained using Opal™ reagents. Tissue sections from primary tumors (lung, melanoma, colorectal, lymphoma)were stained against 5 markers of key interest in the field of immune oncology (CD8, FoxP3, PD-1, PD-L1, CD68) along with a tumor marker (Cytokeratin, Sox10&S100, PAX5 or PAX8) and DAPI counterstain, resulting in 7 colors on each slide. Conventional and multispectral digital scans were acquired on a Vectra Polaris® automated imaging system and analyzed with inForm® and R software using the phenoptr and phenoptrReports packages. Results Multispectral scans showed a wide variety of immunological states among samples and within samples. We observed differences in the overall composition and density of immune cells in the tumor microenvironment, and further distinctions when looking at the local proximities of cell types and cells expressing either PD-1 or PD-L1, based on marker combinations that could not be observed with a conventional 3- or 4-color slide scan. We developed spatial analyses to visualize and quantify this observed heterogeneity, both across the entire tissue and along the invasive margin. These tools revealed hot spots of immune activity and were further used to compare and categorize the properties of different tumors. Furthermore, when compared with results from analogous scans analyzed without spectral unmixing, the unmixed imagery showed a marked improvement in the dynamic range of detected positive cells. Conclusion These tools for spatial analyses of whole-section 7-color multispectral slide scans provide translational researchers the opportunity to discover and validate biomarkers that capture immuno-biological interactions at microscopic and macroscopic scales, to better understand drug method-of-action and why some patients respond and some don’t. Citation Format: Carla Coltharp, Kent Johnson, Wenliang Zhang, Chichung Wang, Kristin Roman, Daniel Eversole, Clifford Hoyt, Peter Miller. Quantifying tumor heterogeneity and mapping complex immune cell interactions with high-throughput, 7-color multispectral slide scans [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 153.

Abstract 1149: Systems pathology by Optra 30 color multiplex immunofluorescence automated whole slide imaging system integrated with image analysis

Abstract Background A paradigm shift from single marker immunohistochemistry (IHC) to multiplexing in molecular histopathology has helped better understand the cellular composition and heterogeneity of sub population of cells in tissues. Presently, one needs to manually exchange filters after each multiplex scan for capturing all fluorophores per staining round. This is very cumbersome and inefficient, as this process requires rescanning the same slide multiple times. Moreover, visual assessment and quantification of biomarkers is limited by inter and intra-observer variability. We hereby present an automated multispectral whole-slide imaging system integrated with image analysis for detection of upto 30-color multiplex assay with spectral unmixing and cell segmentation functionality. Material and Methods Multiplex IHC whole slide imaging Formalin Fixed Paraffin Embedded rat brain; indirect IHC stained sections with 10 antibodies using photostable fluorophores were obtained. The staining was based on antigenic integrity and antibody binding affinity. Optra's multispectral fluorescent whole slide scanning process was optimized to image sensitive antigens in early cycles and robust antigens in latter cycles. The fully automated fluorescence scanning system imaged weakly expressing biomarkers in the visible range (480-620 nm), highly expressing targets in the violet/blue (405-440 nm), far-red (650-680 nm) and NIR (700-800 nm) range across the entire usable light spectrum (350-800 nm). ResultsWe could simultaneously detect up to 30 biomarkers within a single tissue section, using 12 staining-imaging-stripping cycles, with 10 antibodies screened per cycle. Software measures enabled separation of spectrally overlapping markers in the multiplex assay. A computer-controlled X-Y-Z stage used autofocusing at 20X/40X magnification. Integrated image analysis solution performed comprehensive evaluation of antigens of interest with accurate quantification of the target proteins based on parameters including but not limited to hue value/width, intensity threshold scoring, morphological characterization, pixel-count thresholding and color saturation. Conclusion This proposed solution is efficient compared to the current imaging methods to tackle the aforementioned image acquisition and image analysis challenges associated with multiplexing assays. Note: This abstract was not presented at the meeting. Citation Format: Abhi Gholap, Anagha P. Jadhav, Suraj Somwanshi, Gurunath Kamble, Isha Doshi, Jiaoti Huang. Systems pathology by Optra 30 color multiplex immunofluorescence automated whole slide imaging system integrated with image analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1149.

Using Convolutional Neural Networks for Cloud Detection from Meteor-M No. 2 MSU-MR Data

A method for cloud detection using the machine-learning algorithm based on a convolutional neural network is presented. Input data are satellite images received from the MSU-MR multispectral low-resolution scanning unit onboard the Meteor-M No. 2 satellite. The developed method can be an alternative to the traditional algorithms of cloud detection based on the calculation of differential indices and thresholds. The algorithm is verified using the machine-learning metrics, comparing the resulting cloud mask with the reference one obtained by interpreting the satellite image by an experienced meteorologist. It was also compared (for verification) with a similar product based on VIIRS spectroradiometer data. The cloud mask computed using the algorithm allows the automatic thematic processing of satellite images.

Comparison of multispectral airborne laser scanning and stereo matching of aerial images as a single sensor solution to forest inventories by tree species

Airborne Light Detection and Ranging (LiDAR) information alone is insufficient for species-specific prediction of forest stand attributes, and therefore auxiliary optical image features (OIF) are commonly used to decrease the prediction errors associated with species-specific tree attributes. However, this requires collection and merging of two data sources, LiDAR and OIF, which increases the costs of the inventory. The recently introduced multispectral LiDAR (M-ALS) provides a potential single-sensor solution for obtaining species-specific information, as its multispectral intensity values resemble optical image data. Image point clouds (IPC) derived from aerial stereo images are another single-sensor option that provides both geometric and optical information. We compared two single-sensor options, M-ALS and IPC, with two LiDAR data sets (leaf-on and leaf-off) with auxiliary OIF, for the prediction of boreal tree species' volumes. In terms of root-mean-square error (RMSE) in the validation data, the LiDAR+OIF combination performed best (leaf-on RMSE: 33.3%; leaf-off RMSE 34.3%), followed by M-ALS + OIF (RMSE = 35.2%) and IPC + OIF (RMSE = 42.4%). The mean RMSE value associated with M-ALS increased to the same level (44.7%) as the IPC + OIF combination when optical image features were not included. Both IPC and M-ALS are potential single sensor solutions for forest inventories, but the use of both LiDAR and OIF provides the most accurate results.

MULTISPECTRAL AIRBORNE LASER SCANNING POINT-CLOUDS FOR LAND COVER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS

Abstract. This paper presents an automated workflow for pixel-wise land cover (LC) classification from multispectral airborne laser scanning (ALS) data using deep learning methods. It mainly contains three procedures: data pre-processing, land cover classification, and accuracy assessment. First, a total of nine raster images with different information were generated from the pre-processed point clouds. These images were assembled into six input data combinations. Meanwhile, the labelled dataset was created using the orthophotos as the ground truth. Also, three deep learning networks were established. Then, each input data combination was used to train and validate each network, which developed eighteen LC classification models with different parameters to predict LC types for pixels. Finally, accuracy assessments and comparisons were done for the eighteen classification results to determine an optimal scheme. The proposed method was tested on six input datasets with three deep learning classification networks (i.e., 1D CNN, 2D CNN, and 3D CNN). The highest overall classification accuracy of 97.2% has been achieved using the proposed 3D CNN. The overall accuracy (OA) of the 2D and 3D CNNs was, on average, 8.4% higher than that of the 1D CNN. Although the OA of the 2D CNN was at most 0.3% lower than that of the 3D CNN, the runtime of the 3D CNN was five times longer than the 2D CNN. Thus, the 2D CNN was the best choice for the multispectral ALS LC classification when considering efficiency. The results demonstrated the proposed methods can successfully classify land covers from multispectral ALS data.

Observation of mesospheric wave using collocated OH airglow temperature and radar wind measurements over Indian low latitude

We analyzed the mesospheric winds and temperature data for investigating the waves scaling from the period of few hours to several hours (and few days) based on the airglow observations at Kolhapur (16.8°N, 74.2°E, 10.6°N dip. lat.). The data presented in this study are collected using medium frequency radar and Multispectral Scanning Photometer at low latitude station Kolhapur. We scrutinized the wind and temperature relation of these waves for the observed period from January to May 2011. The data of 56 clear nights were collected and out of which 22 nights of data shows a conspicuous wavelike features. The nocturnal variability reveals the prominent wave signatures with a period which range from 7 to 12 h (h) as a dominant nocturnal wave. The presence of quasi 2.8–4 days waveswith significant amplitudes is also detected. The comparison of the winds and temperatures suggests the temperature waves to be near in phase with meridional wind component and a time delayedrelation with the zonal wind component.

Calibration model of polarimeters on board the Aerosol-UA space mission

The polarimetric model has been developed for calibration of the multispectral Scanning Polarimeter (ScanPol) and the MultiSpectral Imaging Polarimeter (MSIP) of the Aerosol-UA space mission. ScanPol and MSIP polarimeters are designed to provide high precision measurements of the atmospheric/earth surface radiation polarization with a substantial cross-track spatial coverage. The main sources of the instrumental errors, such as the finite extinction ratios of polarizers and the phase retards of telescopes are simulated in the polarimetric model. The mirrors mismatching, dark reference signal, the difference in the polarimeter channels gain, and the instrumental depolarization factor are modeled as additive values and scalar multipliers. The polarimetric model is derived in the form of Mueller matrices, which describe the transformation from the incoming polarized radiation to the measured signal. The parameters of the model are estimated experimentally. The numerical experiments have been carried out to validate the effectiveness of the proposed calibration procedures. The results have shown the possibility to minimize the ScanPol polarization errors below 0.15% (DoLP) and 0.2° (AoLP), and the MSIP polarization errors below 1%. The set of two polarimeters, namely the along-track scanner and the wide-angle imager, allows providing the in-orbit cross-calibration of the MSIP using the ScanPol measurements when their fields of view are overlapped. The concept of the MSIP and ScanPol polarimeters cross-calibration is discussed in the paper.

Near-infrared multispectral endoscopic imaging of deep artificial interproximal lesions in extracted teeth.

A safer alternative method to radiographic imaging is needed. We present a multispectral near-infrared scanning fiber endoscope (nirSFE) for dental imaging which is designed to be the smallest imaging probe with near-infrared (NIR) imaging (1200-2000 nm). The prototype nirSFE is designed for wide-field forward viewing of scanned laser illumination at 1310, 1460, or 1550 nm. Artificial lesions with varying sizes and locations were prepared on proximal surfaces of extracted human teeth to examine capability and limitation of this new dental imaging modality. Nineteen artificial interproximal lesions and several natural occlusal lesions on extracted teeth were imaged with nirSFE, OCT, and microCT. Our nirSFE system has a flexible shaft as well as a probe tip with diameter of 1.6 mm and a rigid length of 9 mm. The small form factor and multispectral NIR imaging capability enables multiple viewing angles and reliable detection of lesions that can extend into the dentin. Among nineteen artificial interproximal lesions, the nirSFE reflectance imaging operating at 1460-nm and OCT operating at 1310-nm scanned illumination exhibited high sensitivity for interproximal lesions that were closer to occlusal surface. Diagnosis from a non-blinded trained user by looking at real-time occlusal-side nirSFE videos indicate true positive rate of 78.9%. There were no false positives. This study demonstrates that nirSFE may be used for detecting occlusal lesions and interproximal lesions located less than 4 mm under the occlusal surface. Major advantages of this imaging system include multiple viewing angles due to flexibility and small form factor, as well as the ability to capture real-time video. The multispectral nirSFE has the potential to be employed as a low-cost dental camera for detecting dental lesions without exposure to ionizing radiation. Lasers Surg. Med. 51:459-465, 2019. © 2019 Wiley Periodicals, Inc.

The Use of a Convolutional Neural Network for Detecting Snow According to the Data of the Multichannel Satellite Device of Meteor-M No.2 Spacecraft

The paper presents a method for detecting snow using a machine learning algorithm based on a convolutional neural network. As the input data, the satellite images obtained from a multispectral low-resolution scanning device (MSU-MR) installed on the Meteor-M No. 2 spacecraft are used. This unit provides images in six visible and infrared channels at 1 km spatial resolution. The developed algorithm can be an alternative to the traditional methods of snow detection, based on calculation the normalized difference snow index (NDSI), the normalized difference vegetation index (NDVI) and threshold values. Evaluation of the algorithm using the f-measure showed a high level of quality of its work. Calculated using the algorithm, the snow mask allows for the construction of maps that can be successfully applied for snow cover monitoring.

The effect of seasonal variation on automated land cover mapping from multispectral airborne laser scanning data

ABSTRACTMultispectral airborne laser scanning (MS-ALS) sensors are a new promising source of data for automated mapping methods. Finding an optimal time for data acquisition is important in all mapping applications based on remotely sensed datasets. In this study, three MS-ALS datasets acquired at different times of the growing season were compared for automated land cover mapping and road detection in a suburban area. In addition, changes in the intensity were studied. An object-based random forest classification was carried out using reference points. The overall accuracy of the land cover classification was 93.9% (May dataset), 96.4% (June) and 95.9% (August). The use of the May dataset acquired under leafless conditions resulted in more complete roads than the other datasets acquired when trees were in leaf. It was concluded that all datasets used in the study are applicable for suburban land cover mapping, however small differences in accuracies between land cover classes exist.

Quantifying the Carbon Storage in Urban Trees Using Multispectral ALS Data

This paper presents a new method for quantifying the carbon storage in urban trees using multispectral airborne laser scanning (ALS) data. This method takes the full advantage of multispectral ALS range and intensity data and shows the feasibility of quantifies the carbon storage in urban trees. Our method consists of four steps: multispectral ALS data processing, vegetation isolation, dendrometric parameters estimation, and carbon storage modeling. Our results suggest that ALS-based dendrometric parameter estimation and allometric models can yield consistent performance and accurate estimation. Citywide carbon storage estimation is derived in this paper for the Town of Whitchurch–Stouffville, Ontario, Canada, by extrapolating the values within the study area to the entire city based on the specific proportion of each land cover type. The proposed method reveals the potential of multispectral ALS data in land cover mapping and carbon storage estimation at individual-tree level.

A new method for 3D individual tree extraction using multispectral airborne LiDAR point clouds

Characterization of individual trees is essential for many applications in forest management and ecology. Previous studies relied on single tree detection from monochromatic wavelength airborne laser scanning (ALS) systems and they focused on the use of the geometric spatial information of the point clouds (i.e., X, Y, and Z coordinates). However, there is quite often a difficulty dealing with clumped trees when only the geometric spatial information is considered. The emergence of multispectral LiDAR sensors provides a new solution for individual tree structure acquisition. The aim of this paper is to investigate the performance of multispectral ALS data for delineating individual trees which are challenging by using the monochromatic wavelength ALS system. The proposed workflow utilizes the mean shift segmentation method on different feature spaces for crown isolation. In addition, both spatial domain and multispectral domain are used to refine the under-segmentation crown segments. Ten plots (2 sets of different structural complexity) located in the dense coniferous forest area in Tobermory, Ontario, Canada are selected as experiment data. Results show that the developed method correctly detects 88% and 82% of the dominant trees with and without multispectral information, respectively. Compared with segmentation using geometric spatial information solely, the main improvements are achieved for clumped tree segment with the distinguished multispectral features. This study demonstrates that multispectral airborne laser scanning data is more capable for individual tree delineation than monochromatic wavelength laser scanning data in dealing with forests with clumped crowns in dense forests.

Extended-resolution imaging of the interaction of lipid droplets and mitochondria

Physical contacts between organelles play a pivotal role in intracellular trafficking of metabolites. Monitoring organelle interactions in living cells using fluorescence microscopy is a powerful approach to functionally assess these cellular processes. However, detailed target acquisition is typically limited due to light diffraction. Furthermore, subcellular compartments such as lipid droplets and mitochondria are highly dynamic and show significant subcellular movement. Thus, high-speed acquisition of these organelles with extended-resolution is appreciated. Here, we present an imaging informatics pipeline enabling spatial and time-resolved analysis of the dynamics and interactions of fluorescently labeled lipid droplets and mitochondria in a fibroblast cell line. The imaging concept is based on multispectral confocal laser scanning microscopy and includes high-speed resonant scanning for fast spatial acquisition of organelles. Extended-resolution is achieved by the recording of images at minimized pinhole size and by post-processing of generated data using a computational image restoration method. Computation of inter-organelle contacts is performed on basis of segmented spatial image data. We show limitations of the image restoration and segmentation part of the imaging informatics pipeline. Since both image processing methods are implemented in other related methodologies, our findings will help to identify artifacts and the false-interpretation of obtained morphometric data. As a proof-of-principle, we studied how lipid load and overexpression of PLIN5, considered to be involved in the tethering of LDs and mitochondria, affects organelle association.

Midinfrared Multispectral Detection for Real-Time and Noninvasive Analysis of the Structure and Composition of Materials.

In situ material identification and object tracking have been demonstrated using a mid-infrared (mid-IR) robotic scanning system. This detection method is capable of inspecting materials noninvasively because the mid-IR spectrum overlaps with numerous characteristic absorption bands corresponding to various chemical function groups. The scanning system consisted of a fiber probe connected to a mid-IR tunable laser with a wavelength tuning range of λ = 2.45-3.75 μm. For the high-speed performance of the scanning system to be evaluated, a testing platform was constructed with an object plate rapidly rotating at ω = 231 rpm. The objects on the plate were SU-8 epoxy-based resin and polydimethylsiloxane, which were mid-IR absorptive while visibly transparent. Applying mid-IR multispectral scanning, the system was able to simultaneously track the object position and identify the composition by interpreting the spectral and spatial intensity variation. The mid-IR robotic scanning method thus provides a visualization system critical for process inspection in automatic manufacturing and high-throughput biomedical screening.

FEATURE RELEVANCE ASSESSMENT OF MULTISPECTRAL AIRBORNE LIDAR DATA FOR TREE SPECIES CLASSIFICATION

Abstract. The presented experiment investigates the potential of Multispectral Laser Scanning (MLS) point clouds for single tree species classification. The basic idea is to simulate a MLS sensor by combining two different Lidar sensors providing three different wavelngthes. The available data were acquired in the summer 2016 at the same date in a leaf-on condition with an average point density of 37 points/m2. For the purpose of classification, we segmented the combined 3D point clouds consisiting of three different spectral channels into 3D clusters using Normalized Cut segmentation approach. Then, we extracted four group of features from the 3D point cloud space. Once a varity of features has been extracted, we applied forward stepwise feature selection in order to reduce the number of irrelevant or redundant features. For the classification, we used multinomial logestic regression with L1 regularization. Our study is conducted using 586 ground measured single trees from 20 sample plots in the Bavarian Forest National Park, in Germany. Due to lack of reference data for some rare species, we focused on four classes of species. The results show an improvement between 4–10 pp for the tree species classification by using MLS data in comparison to a single wavelength based approach. A cross validated (15-fold) accuracy of 0.75 can be achieved when all feature sets from three different spectral channels are used. Our results cleary indicates that the use of MLS point clouds has great potential to improve detailed forest species mapping.

Laser Retinotomy with “Ultra Q Reflex” System for the Prevention of Rhegmatogenous Retinal Detachment due to the Peripheral Horseshoe Tears

Purpose. To evaluate laser retinotomy efficacy in the retinal detachment (RD) due horseshoe tear prevention. Patients and methods. 57 eyes of 54 patients with subclinical retinal detachment due to the peripheral horseshoe retinal tear were enrolled. 26 eyes of 25 patients of main group undergone combined laser technology included a barrier retinal photocoagulation of the local detachment zone, accompanied with deffered Nd:YAG laser retinotomy. The barrier retinal photocoagulation alone was performed on 31 eyes of 29 patients in control group to compare postoperation results. The assessment of the peripheral vitreoretinal interface was carried out by fundus ultrasound scanning, multispectral scanning laser imaging and spectral optical coherence tomography. The efficacy of the laser treatment was estimated by local retinal detachment height and area changes, while the safety was estimated by intra- and post-op complications rate. All patients were examined at the baseline, 1 week, 1, 3, 6 and 12 month follow-up. Results. Both main and control groups undergone barrier laser photocoagulation with complete round demarcation of subclinical local retinal detachment. All twenty five patients of main group achieved successful laser resection of retinal horseshore flap without any evidence of clinically significant complications. YAG-laser retinotomy in patients with retinal horseshoe tear resulted in decreased values of local retinal detachment height and area (in 40 and 38% respectively, p < 0.05). At the 12-month follow-up full retinal adhesion has been achieved in 15 eyes (58%), while partial adhesion has been observed in 7 eyes (27%). Obtained data were significantly lower compared with control group where some sings of retinal detachment progression were appeared (in 11 and 4% respectively, p > 0.05). No evidence of clinically significant complications after laser treatment has been revealed. Conclusions. The obtained data have demonstrated the efficacy and the safety of laser retinotomy in traction component reducing and consequent risk of retinal detachment minimising.