Laryngoscopic Image Stitching for View Enhancement and Documentation - First Experiences

One known problem within laryngoscopy is the spatially limited view onto the hypopharynx and the larynx through the endoscope. To examine the complete larynx and hypopharynx, the laryngoscope can be rotated about its main axis, and hence the physician obtains a complete view. If such examinations are captured using endoscopic video, the examination can be reviewed in detail at a later time. Nevertheless, in order to document the examination with a single representative image, a panorama image can be computed for archiving and enhanced documentation. Twenty patients with various clinical findings were examined with a 70 rigid laryngoscope, and the video sequences were digitally stored. The image sequence for each patient was then post-processed using an image stitching tool based on SIFT features, the RANSAC approach and blending. As a result, endoscopic panorama images of the larynx and pharynx were obtained for each video sequence. The proposed approach of image stitching for laryngoscopic video sequences offers a new tool for enhanced visual examination and documentation of morphologic characteristics of the larynx and the hypopharynx

AN INCREMENTAL BASED APPROACH FOR 3D MULTI-ANGLE POINT CLOUD STITCHING USING ICP AND KNN

The basic principle of stitching is joining or merging any two materials or objects. 3D point cloud stitching is basically stitching two 3D point cloud together. 3D point cloud stitching is an emerging topic and there are multiple ways to achieve it. There are various methods for stitching which all have changes throughout the time. The existing methods do have shortcomings and have ignored the multiangle stitching of a same model or an object. This shortfall leads to many deficiencies in the ability of a stitching algorithm to maintain accuracy over the period. In this work I have introduced a new approach for an iterative based approach for 3d multi-angle point cloud stitching using ICP (Iterative closest point algorithm) and KNN (K-nearest neighbor). The design follows an incremental approach to achieve the results. This is a novel approach of stitching multiple 3D point clouds taken from multiple angles of a single bust. The framework is evaluated based on the stitching results provided by the algorithm capability of stitching multiple point cloud into a solid model

Serial optical coherence microscopy for label-free volumetric histopathology

The observation of histopathology using optical microscope is an essential procedure for examination of tissue biopsies or surgically excised specimens in biological and clinical laboratories. However, slide-based microscopic pathology is not suitable for visualizing the large-scale tissue and native 3D organ structure due to its sampling limitation and shallow imaging depth. Here, we demonstrate serial optical coherence microscopy (SOCM) technique that offers label-free, high-throughput, and large-volume imaging of ex vivo mouse organs. A 3D histopathology of whole mouse brain and kidney including blood vessel structure is reconstructed by deep tissue optical imaging in serial sectioning techniques. Our results demonstrate that SOCM has unique advantages as it can visualize both native 3D structures and quantitative regional volume without introduction of any contrast agents

Deep learning-based diatom taxonomy on virtual slides

Kloster M, Langenkämper D, Zurowietz M, Beszteri B, Nattkemper TW. Deep learning-based diatom taxonomy on virtual slides. Scientific Reports. 2020;10(1): 14416

A Method for Automatic Image Rectification and Stitching for Vehicle Yaw Marks Trajectory Estimation

The aim of this study has been to propose a new method for automatic rectification and stitching of the images taken on the accident site. The proposed method does not require any measurements to be performed on the accident site and thus it is frsjebalaee of measurement errors. The experimental investigation was performed in order to compare the vehicle trajectory estimation according to the yaw marks in the stitched image and the trajectory, reconstructed using the GPS data. The overall mean error of the trajectory reconstruction, produced by the method proposed in this paper was 0.086 m. It was only 0.18% comparing to the whole trajectory length.</p

Optical quality assurance of GEM foils

An analysis software was developed for the high aspect ratio optical scanning system in the Detec- tor Laboratory of the University of Helsinki and the Helsinki Institute of Physics. The system is used e.g. in the quality assurance of the GEM-TPC detectors being developed for the beam diagnostics system of the SuperFRS at future FAIR facility. The software was tested by analyzing five CERN standard GEM foils scanned with the optical scanning system. The measurement uncertainty of the diameter of the GEM holes and the pitch of the hole pattern was found to be 0.5 {\mu}m and 0.3 {\mu}m, respectively. The software design and the performance are discussed. The correlation between the GEM hole size distribution and the corresponding gain variation was studied by comparing them against a detailed gain mapping of a foil and a set of six lower precision control measurements. It can be seen that a qualitative estimation of the behavior of the local variation in gain across the GEM foil can be made based on the measured sizes of the outer and inner holes.Comment: 12 pages, 29 figure

Virtual Microscopy with Extended Depth of Field

In this paper, we describe a virtual microscope system, based on JPEG 2000, which utilizes extended depth of field (EDF) imaging. Through a series of observer trials we show that EDF imaging improves both the local image quality of individual fields of view (FOV) and the accuracy with which the FOVs can be mosaiced (stitched) together. In addition, we estimate the required bit rate to adequately render a set of histology and cytology specimens at a quality suitable for on-line learning and collaboration. We show that, using JPEG 2000, we can efficiently represent high-quality, high-resolution colour images of microscopic specimens with less than 1 bit per pixel

An in Depth Review Paper on Numerous Image Mosaicing Approaches and Techniques

Image mosaicing is one of the most important subjects of research in computer vision at current. Image mocaicing requires the integration of direct techniques and feature based techniques. Direct techniques are found to be very useful for mosaicing large overlapping regions, small translations and rotations while feature based techniques are useful for small overlapping regions. Feature based image mosaicing is a combination of corner detection, corner matching, motion parameters estimation and image stitching.Furthermore, image mosaicing is considered the process of obtaining a wider field-of-view of a scene from a sequence of partial views, which has been an attractive research area because of its wide range of applications, including motion detection, resolution enhancement, monitoring global land usage, and medical imaging. Numerous algorithms for image mosaicing have been proposed over the last two decades.In this paper the authors present a review on different approaches for image mosaicing and the literature over the past few years in the field of image masaicing methodologies. The authors take an overview on the various methods for image mosaicing.This review paper also provides an in depth survey of the existing image mosaicing algorithms by classifying them into several groups. For each group, the fundamental concepts are first clearly explained. Finally this paper also reviews and discusses the strength and weaknesses of all the mosaicing groups

Quantitative wood anatomy

Quantitative wood anatomy analyzes the variability of xylem anatomical features in trees, shrubs, and herbaceous species to address research questions related to plant functioning, growth, and environment. Among the more frequently considered anatomical features are lumen dimensions and wall thickness of conducting cells, fibers, and several ray properties. The structural properties of each xylem anatomical feature are mostly fixed once they are formed, and define to a large extent its functionality, including transport and storage of water, nutrients, sugars, and hormones, and providing mechanical support. The anatomical features can often be localized within an annual growth ring, which allows to establish intra-annual past and present structure-function relationships and its sensitivity to environmental variability. However, there are many methodological challenges to handle when aiming at producing (large) data sets of xylem anatomical data. Here we describe the different steps from wood sample collection to xylem anatomical data, provide guidance and identify pitfalls, and present different image-analysis tools for the quantification of anatomical features, in particular conducting cells. We show that each data production step from sample collection in the field, microslide preparation in the lab, image capturing through an optical microscope and image analysis with specific tools can readily introduce measurement errors between 5 and 30% and more, whereby the magnitude usually increases the smaller the anatomical features. Such measurement errors—if not avoided or corrected—may make it impossible to extract meaningful xylem anatomical data in light of the rather small range of variability in many anatomical features as observed, for example, within time series of individual plants. Following a rigid protocol and quality control as proposed in this paper is thus mandatory to use quantitative data of xylem anatomical features as a powerful source for many research topics