Peripheral neuropathy can be caused by diabetes or AIDS or be a side-effect of chemotherapy. Fibered Fluorescence Microscopy (FFM) is a recently developed imaging modality using a fiber optic probe connected to a laser scanning unit. It allows for in-vivo scanning of small animal subjects by moving the probe along the tissue surface. In preclinical research, FFM enables non-invasive, longitudinal in vivo assessment of intra epidermal nerve fibre density in various
models for peripheral neuropathies. By moving the probe, FFM allows visualization of larger surfaces, since, during the
movement, images are continuously captured, allowing to acquire an area larger then the field of view of the probe. For analysis purposes, we need to obtain a single static image from the multiple overlapping frames. We introduce a mosaicing procedure for this kind of video sequence. Construction of mosaic images with sub-pixel alignment is indispensable and must be integrated into a global consistent image aligning. An additional motivation for the mosaicing is the use of overlapping redundant information to improve the signal to noise ratio of the acquisition, because the individual frames tend to have both high noise levels and intensity inhomogeneities. For longitudinal analysis, mosaics captured at different times must be aligned as well. For alignment, global correlation-based matching is compared with interest point matching. Use of algorithms working on multiple CPU's (parallel processor/cluster/grid) is imperative for use in a screening model.
Bias field reduction is a common problem in medical imaging. A bias field usually manifests itself as a smooth intensity variation across the image. The resulting image inhomogeneity is a severe problem for posterior image processing and analysis techniques such as registration or segmentation. In this paper, we present a fast debiasing technique based on localized Lloyd-Max quantization. Thereby, the local bias is modelled as a multiplicative field and is assumed to be slowly varying. The method is based on the assumption that the local, undegraded histogram is characterized by a limited number of gray values. The goal is then to find the discrete intensity values such that spreading those values according to the local bias field reproduces the global histogram as good as possible. We show that our method is capable of efficiently reducing (even strong) bias fields in 3D volumes in only a few seconds.
Purpose: Voxel based morphometry (VBM) is increasingly being used to detect diffusion tensor
(DT) image abnormalities in patients for different pathologies. An important requisite for these VBM studies
is the use of a high-dimensional, non-rigid coregistration technique, which is able to align both the spatial and
the orientational information. Recent studies furthermore indicate that high-dimensional DT information should
be included during coregistration for an optimal alignment. In this context, a population based DTI atlas
is created that preserves the orientational DT information robustly and contains a minimal bias towards any
specific individual data set. Methods: A ground truth evaluation method is developed using a single subject
DT image that is deformed with 20 deformation fields. Thereafter, an atlas is constructed based on these 20
resulting images. Thereby, the non-rigid coregistration algorithm is based on a viscous fluid model and on mutual
information. The fractional anisotropy (FA) maps as well as the DT elements are used as DT image information
during the coregistration algorithm, in order to minimize the orientational alignment inaccuracies. Results:
The population based DT atlas is compared with the ground truth image using accuracy and precision measures
of spatial and orientational dependent metrics. Results indicate that the population based atlas preserves the
orientational information in a robust way. Conclusion: A subject independent population based DT atlas is
constructed and evaluated with a ground truth method. This atlas contains all available orientational information
and can be used in future VBM studies as a reference system.
In this paper, we study denoising of multicomponent images. We present a framework of spatial wavelet-based
denoising techniques, based on Bayesian least-squares optimization procedures, using prior models for the wavelet
coefficients that account for the correlations between the image components. Within this framework, multicomponent
prior models for the wavelet coefficients are required that a) fully account for the interband correlations
between the image components, and b) approximate well the marginal distributions of the wavelet coefficients.
For this, multicomponent heavy tailed models are applied. We analyze three mixture priors: Gaussian scale
mixture (GSM) models, Laplacian mixture models and Bernoulli-Gaussian mixture models. As an extension of
the Bayesian framework, we propose a framework that also accounts for the correlation between the multicomponent
image and an auxiliary noise-free image, in order to improve the SNR of the first. For this, a GSM prior
model was applied. Experiments are conducted in the domain of remote sensing in both, simulated and real
Proc. SPIE. 6763, Wavelet Applications in Industrial Processing V
KEYWORDS: Signal to noise ratio, Wavelet transforms, Principal component analysis, Wavelets, Denoising, Image restoration, Fourier transforms, Deconvolution, Global system for mobile communications, Expectation maximization algorithms
In this paper we study the restoration of multicomponent images, and more particularly, the effects of taking into account the dependencies between the image components. The used method is an expectation-maximization algorithm, which applies iteratively a deconvolution and a denoising step. It exploits the Fourier transform's economical noise representation for deconvolution, and the wavelet transform's economical representation of piecewise smooth images for denoising. The proposed restoration procedure performs wavelet shrinkage in a Bayesian denoising framework by applying multicomponent probability density models for the wavelet coefficients that fully account for the intercomponent correlations. In the experimental section, we compare our multicomponent procedures to its single-component counterpart. The results show that the methods using a multicomponent model and especially the one using the Gaussian scale mixture model, perform better than the single-component procedure.
A technique is presented for detecting vegetation crop nutrient stress from hyperspectral data. Experiments are conducted on peach trees. It is shown that nutrient deficiencies that caused stress could an be detected reliably on hyperspectral spectra. During an extensive field campaign, foliar and crown reflectance has been measured with a portable field spectroradiometer. Airborne hyperspectral imagery is acquired over the orchard with the AHS hyperspectral sensor. The multi-level approach (leaf level and top of canopy) enabled the assessment of vegetation indices and their relationship with pigment concentration at both leaf and canopy levels, showing the potential and limitations of hyperspectral remote sensing on the different levels. Stress on the peach orchard is was treated with iron chelates to recover from iron chlorosis conditions. Blocks of trees treated with iron chelates created a dynamic range of chlorophyll concentration as measured in leaves. A relationship is obtained between the measured spectra and estimated biochemical parameters via inversion of a linked directional homogeneous canopy reflectance model (ACRM) and the PROSPECT leaf model. Numerical model inversion was conducted by minimizing the difference between the measured reflectance samples and modeled values. An improved optimization method is presented. Results are compared with a simple linear regression analysis, linking chlorophyll to the reflectance measured at the leaf level and Top of Canopy (TOC). Optimal band regions and bandwidths are analyzed.
In this paper, a wavelet-based enhancement method for multicomponent images or image series is proposed. The method applies Bayesian estimation, including the use of a high-resolution noise-free grey scale image as prior information. The resulting estimator statistically exploits the correlation between the image series and the high-resolution noise-free image to enhance (i.e. to improve the signal to noise ratio and the spatial resolution) of the image series. To validate and demonstrate the procedure, results are shown on a color image. The idea of using an auxiliary image can be applied in many different domains. As an example, experiments are conducted in two different application domains: resolution enhancement of multispectral remote sensing images and improvement of brain activity measurements on functional MRI image time series.
Hyperspectral image classification impose challenging requirements to
a classifier. It is well known that more spectral bands can be difficult to process and introduce problems such as the Hughes phenomenon. Nevertheless, user requirements are very demanding, as expectations grow with the available number of spectral bands: subtle differences in a large number of classes must be distinguished. As multiclass classifiers become rather complex for a large number of classes, a combination of binary classification results are often used to come to a class decision. In this approach, the posterior probability is retained for each of the binary classifiers. From these, a combined posterior probability for the multiclass case is obtained. The proposed technique is applied to map the highly diverse Belgian coastline. In total, 17 vegetation types are defined. Additionally, bare soil, shadow, water and urban area are also classified. The posterior probabilities are used for unmixing. This is demonstrated for 4 classes: bare soil and 3 vegetation classes. Results are very promosing, outperforming other approaches such as linear unmixing.
This paper studies the detection of vegetation stress in orchards via remote sensing. During previous research, it was shown that stress can be detected reliably on hyperspectral reflectances of the fresh leaves, using a generic wavelet based hyperspectral classification. In this work, we demonstrate the capability to detect stress from airborne/spaceborne hyperspectral sensors by upscaling the leaf reflectances to top of atmosphere (TOA) radiances. Several data sets are generated, measuring the foliar reflectance with a portable field spectroradiometer, covering different time periods, fruit variants and stress types. We concentrated on the Jonagold and Golden Delicious apple trees, induced with mildew and nitrogen deficiency. First, a directional homogeneous canopy reflectance model (ACRM) is applied on these data sets for simulating top of canopy (TOC) spectra. Then, the TOC level is further upscaled to TOA, using the atmospheric radiative transfer model MODTRAN4. To simulate hyperspectral imagery acquired with real airborne/spaceborne sensors, the spectrum is further filtered and subsampled to the available resolution. Using these simulated upscaled TOC and TOA spectra in classification, we will demonstrate that there is still a differentiation possible between stresses and non-stressed trees. Furthermore, results show it is possible to train a classifier with simulated TOA data, to make a classification of real hyperspectral imagery over the orchard.
Proc. SPIE. 5238, Image and Signal Processing for Remote Sensing IX
KEYWORDS: Statistical analysis, FDA class I medical device development, Wavelets, Remote sensing, Reflectivity, Linear filtering, Discrete wavelet transforms, Feature extraction, Vegetation, Feature selection
The high spectral and high spatial resolution, intrinsic to hyperspectral remote sensing, result in huge quantities of data, which slows down the data processing and can result in a poor performance of classifiers. To improve the classification performance, efficient feature extraction methods are needed. This paper introduces a set of features based on the discrete wavelet transform (DWT). Wavelet coefficients, wavelet energies and wavelet detail histogram features are employed as new features for classification. As a feature reduction procedure, we propose a sequential floating search method. Selection is performed using a cost function based on the estimated probability of error, using the Fisher criterion. This procedure selects the best combination of features. To demonstrate the proposed wavelet features and selection procedure, we apply it to vegetation stress detection. For this application, it is shown that wavelet coefficients outperform spectral reflectance and that the proposed selection procedure outperforms combining the best single features.