15,314 research outputs found
Three-dimensional reconstruction of the tissue-specific multielemental distribution within Ceriodaphnia dubia via multimodal registration using laser ablation ICP-mass spectrometry and X-ray spectroscopic techniques
In this work, the three-dimensional elemental, distribution profile within the freshwater crustacean Ceriodaphnia dubia was constructed at a spatial resolution down to S mu m via a data, fusion approach employing state-of-the-art laser ablation inductively coupled plasma-time-of-flight mass spectrometry (LAICP-TOFMS) and laboratory-based absorption microcomputed tomography (mu-CT). C. dubia was exposed to elevated Cu, Ni, and Zn concentrations, chemically fixed, dehydrated, stained, and embedded, prior to mu-CT analysis. Subsequently, the sample was cut into 5 pm thin sections that were subjected to LA-ICPTOFMS imaging. Multimodal image registration was performed to spatially align the 2D LA-ICP-TOFMS images relative to the Corresponding slices of the 3D mu-CT reconstruction. Mass channels corresponding to the isotopes of a single element were merged to improve the signal-to-noise ratios within the elemental images. In order to aid the visual interpretation of the data, LA-ICP-TOEMS data wete projected onto the mu-CT voxels representing tissue. Additionally, the image resolution and elemental sensitivity were compared to those obtained with synchrotron radiation based 3D confocal mu-X-ray fluorescence imaging upon a chemically fixed and air-dried C. dubia specimen
Optimization for automated assembly of puzzles
The puzzle assembly problem has many application areas such as restoration and reconstruction of archeological findings, repairing of broken objects, solving jigsaw type puzzles, molecular docking problem, etc. The puzzle pieces usually include not only geometrical shape information but also visual information such as texture, color, and continuity of lines. This paper presents a new approach to the puzzle assembly problem that is based on using textural features and geometrical constraints. The texture of a band outside the border of pieces is predicted by inpainting and texture synthesis methods. Feature values are derived from these original and predicted images of pieces. An affinity measure of corresponding pieces is defined and alignment of the puzzle pieces is formulated as an optimization problem where the optimum assembly of the pieces is achieved by maximizing the total affinity measure. An fft based image registration technique is used to speed up the alignment of the pieces. Experimental results are presented on real and artificial data sets
2D Reconstruction of Small Intestine's Interior Wall
Examining and interpreting of a large number of wireless endoscopic images
from the gastrointestinal tract is a tiresome task for physicians. A practical
solution is to automatically construct a two dimensional representation of the
gastrointestinal tract for easy inspection. However, little has been done on
wireless endoscopic image stitching, let alone systematic investigation. The
proposed new wireless endoscopic image stitching method consists of two main
steps to improve the accuracy and efficiency of image registration. First, the
keypoints are extracted by Principle Component Analysis and Scale Invariant
Feature Transform (PCA-SIFT) algorithm and refined with Maximum Likelihood
Estimation SAmple Consensus (MLESAC) outlier removal to find the most reliable
keypoints. Second, the optimal transformation parameters obtained from first
step are fed to the Normalised Mutual Information (NMI) algorithm as an initial
solution. With modified Marquardt-Levenberg search strategy in a multiscale
framework, the NMI can find the optimal transformation parameters in the
shortest time. The proposed methodology has been tested on two different
datasets - one with real wireless endoscopic images and another with images
obtained from Micro-Ball (a new wireless cubic endoscopy system with six image
sensors). The results have demonstrated the accuracy and robustness of the
proposed methodology both visually and quantitatively.Comment: Journal draf
Whole slide image registration for the study of tumor heterogeneity
Consecutive thin sections of tissue samples make it possible to study local
variation in e.g. protein expression and tumor heterogeneity by staining for a
new protein in each section. In order to compare and correlate patterns of
different proteins, the images have to be registered with high accuracy. The
problem we want to solve is registration of gigapixel whole slide images (WSI).
This presents 3 challenges: (i) Images are very large; (ii) Thin sections
result in artifacts that make global affine registration prone to very large
local errors; (iii) Local affine registration is required to preserve correct
tissue morphology (local size, shape and texture). In our approach we compare
WSI registration based on automatic and manual feature selection on either the
full image or natural sub-regions (as opposed to square tiles). Working with
natural sub-regions, in an interactive tool makes it possible to exclude
regions containing scientifically irrelevant information. We also present a new
way to visualize local registration quality by a Registration Confidence Map
(RCM). With this method, intra-tumor heterogeneity and charateristics of the
tumor microenvironment can be observed and quantified.Comment: MICCAI2018 - Computational Pathology and Ophthalmic Medical Image
Analysis - COMPA
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