8,734 research outputs found
Visual Quality Enhancement in Optoacoustic Tomography using Active Contour Segmentation Priors
Segmentation of biomedical images is essential for studying and
characterizing anatomical structures, detection and evaluation of pathological
tissues. Segmentation has been further shown to enhance the reconstruction
performance in many tomographic imaging modalities by accounting for
heterogeneities of the excitation field and tissue properties in the imaged
region. This is particularly relevant in optoacoustic tomography, where
discontinuities in the optical and acoustic tissue properties, if not properly
accounted for, may result in deterioration of the imaging performance.
Efficient segmentation of optoacoustic images is often hampered by the
relatively low intrinsic contrast of large anatomical structures, which is
further impaired by the limited angular coverage of some commonly employed
tomographic imaging configurations. Herein, we analyze the performance of
active contour models for boundary segmentation in cross-sectional optoacoustic
tomography. The segmented mask is employed to construct a two compartment model
for the acoustic and optical parameters of the imaged tissues, which is
subsequently used to improve accuracy of the image reconstruction routines. The
performance of the suggested segmentation and modeling approach are showcased
in tissue-mimicking phantoms and small animal imaging experiments.Comment: Accepted for publication in IEEE Transactions on Medical Imagin
Multi texture analysis of colorectal cancer continuum using multispectral imagery
Purpose
This paper proposes to characterize the continuum of colorectal cancer (CRC) using multiple texture features extracted from multispectral optical microscopy images. Three types of pathological tissues (PT) are considered: benign hyperplasia, intraepithelial neoplasia and carcinoma.
Materials and Methods
In the proposed approach, the region of interest containing PT is first extracted from multispectral
images using active contour segmentation. This region is then encoded using texture features based on the Laplacian-of-Gaussian (LoG) filter, discrete wavelets (DW) and gray level co-occurrence matrices (GLCM). To assess the significance of textural differences between PT types, a statistical analysis based on the Kruskal-Wallis test is performed. The usefulness of texture features is then evaluated quantitatively in terms of their ability to predict PT types using various classifier models.
Results
Preliminary results show significant texture differences between PT types, for all texture features (p-value < 0.01). Individually, GLCM texture features outperform LoG and DW features in terms of PT type prediction. However, a higher performance can be achieved by combining all texture features, resulting in a mean classification accuracy of 98.92%, sensitivity of 98.12%, and specificity of 99.67%.
Conclusions
These results demonstrate the efficiency and effectiveness of combining multiple texture features for characterizing the continuum of CRC and discriminating between pathological tissues in multispectral images
Online Mutual Foreground Segmentation for Multispectral Stereo Videos
The segmentation of video sequences into foreground and background regions is
a low-level process commonly used in video content analysis and smart
surveillance applications. Using a multispectral camera setup can improve this
process by providing more diverse data to help identify objects despite adverse
imaging conditions. The registration of several data sources is however not
trivial if the appearance of objects produced by each sensor differs
substantially. This problem is further complicated when parallax effects cannot
be ignored when using close-range stereo pairs. In this work, we present a new
method to simultaneously tackle multispectral segmentation and stereo
registration. Using an iterative procedure, we estimate the labeling result for
one problem using the provisional result of the other. Our approach is based on
the alternating minimization of two energy functions that are linked through
the use of dynamic priors. We rely on the integration of shape and appearance
cues to find proper multispectral correspondences, and to properly segment
objects in low contrast regions. We also formulate our model as a frame
processing pipeline using higher order terms to improve the temporal coherence
of our results. Our method is evaluated under different configurations on
multiple multispectral datasets, and our implementation is available online.Comment: Preprint accepted for publication in IJCV (December 2018
Counterfeit Detection with Multispectral Imaging
Multispectral imaging is becoming more practical for a variety of applications due to its ability to provide hyper specific information through a non-destructive analysis. Multispectral imaging cameras can detect light reflectance from different spectral bands of visible and nonvisible wavelengths. Based on the different amount of band reflectance, information can be deduced on the subject. Counterfeit detection applications of multispectral imaging will be decomposed and analyzed in this thesis. Relations between light reflectance and objects’ features will be addressed. The process of the analysis will be broken down to show how this information can be used to provide more insight on the object. This technology provides desired and viable information that can greatly improve multiple fields. For this paper, the multispectral imaging research process of element solution concentrations and counterfeit detection applications of multispectral imaging will be discussed. BaySpec’s OCI-M Ultra Compact Multispectral Imager is used for data collection. This camera is capable of capturing light reflectance from wavelengths of 400 – 1000 nm. Further research opportunities of developing self-automated unmanned aerial vehicles for precision agriculture and extending counterfeit detection applications will also be explored
Multispectral fingerprinting for improved in vivo cell dynamics analysis
Background:
Tracing cell dynamics in the embryo becomes tremendously difficult when cell trajectories cross in space and time and tissue density obscure individual cell borders. Here, we used the chick neural crest (NC) as a model to test multicolor cell labeling and multispectral confocal imaging strategies to overcome these roadblocks.
Results:
We found that multicolor nuclear cell labeling and multispectral imaging led to improved resolution of in vivo NC cell identification by providing a unique spectral identity for each cell. NC cell spectral identity allowed for more accurate cell tracking and was consistent during short term time-lapse imaging sessions. Computer model simulations predicted significantly better object counting for increasing cell densities in 3-color compared to 1-color nuclear cell labeling. To better resolve cell contacts, we show that a combination of 2-color membrane and 1-color nuclear cell labeling dramatically improved the semi-automated analysis of NC cell interactions, yet preserved the ability to track cell movements. We also found channel versus lambda scanning of multicolor labeled embryos significantly reduced the time and effort of image acquisition and analysis of large 3D volume data sets.
Conclusions:
Our results reveal that multicolor cell labeling and multispectral imaging provide a cellular fingerprint that may uniquely determine a cell's position within the embryo. Together, these methods offer a spectral toolbox to resolve in vivo cell dynamics in unprecedented detail
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