KEYWORDS: Absorbance, In vivo imaging, Reflectivity, Hyperspectral imaging, Image analysis, In vitro testing, Atrial fibrillation, Cameras, Eye, Data modeling
We investigated the feasibility of a novel method for hyperspectral mapping of macular pigment (MP) in vivo. Six healthy subjects were recruited for noninvasive imaging using a snapshot hyperspectral system. The three-dimensional full spatial-spectral data cube was analyzed using non-negative matrix factorization (NMF), wherein the data was decomposed to give spectral signatures and spatial distribution, in search for the MP absorbance spectrum. The NMF was initialized with the in vitro MP spectrum and rank 4 spectral signature decomposition was used to recover the MP spectrum and optical density in vivo. The recovered MP spectra showed two peaks in the blue spectrum, characteristic of MP, giving a detailed in vivo demonstration of these absorbance peaks. The peak MP optical densities ranged from 0.08 to 0.22 (mean 0.15+/−0.05) and became spatially negligible at diameters 1100 to 1760 μm (4 to 6 deg) in the normal subjects. This objective method was able to exploit prior knowledge (the in vitro MP spectrum) in order to extract an accurate in vivo spectral analysis and full MP spatial profile, while separating the MP spectra from other ocular absorbers. Snapshot hyperspectral imaging in combination with advanced mathematical analysis provides a simple cost-effective approach for MP mapping in vivo.
Labeled training data in the medical domain is rare and expensive to obtain. The lack of labeled multimodal medical
image data is a major obstacle for devising learning-based interactive segmentation tools. Transductive learning (TL) or
semi-supervised learning (SSL) offers a workaround by leveraging unlabeled and labeled data to infer labels for the test
set given a small portion of label information. In this paper we propose a novel algorithm for interactive segmentation
using transductive learning and inference in conditional mixture naïve Bayes models (T-CMNB) with spatial
regularization constraints. T-CMNB is an extension of the transductive naïve Bayes algorithm [1, 20]. The multimodal
Gaussian mixture assumption on the class-conditional likelihood and spatial regularization constraints allow us to
explain more complex distributions required for spatial classification in multimodal imagery. To simplify the estimation
we reduce the parameter space by assuming naïve conditional independence between the feature space and the class
label. The naïve conditional independence assumption allows efficient inference of marginal and conditional
distributions for large scale learning and inference [19]. We evaluate the proposed algorithm on multimodal MRI brain
imagery using ROC statistics and provide preliminary results. The algorithm shows promising segmentation
performance with a sensitivity and specificity of 90.37% and 99.74% respectively and compares competitively to
alternative interactive segmentation schemes.
Computer aided diagnosis in the medical image domain requires sophisticated probabilistic models to formulate
quantitative behavior in image space. In the diagnostic process detailed knowledge of model performance with respect to
accuracy, variability, and uncertainty is crucial. This challenge has lead to the fusion of two successful learning schools
namely generative and discriminative learning. In this paper, we propose a generative-discriminative learning approach
to predict object boundaries in medical image datasets. In our approach, we perform probabilistic model matching of
both modeling domains to fuse into the prediction step appearance and structural information of the object of interest
while exploiting the strength of both learning paradigms. In particular, we apply our method to the task of true-false
lumen segmentation of aortic dissections an acute disease that requires automated quantification for assisted medical
diagnosis. We report empirical results for true-false lumen discrimination of aortic dissection segmentation showing
superior behavior of the hybrid generative-discriminative approach over their non hybrid generative counterpart.
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