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PubMed1998

Two-dimensional Gabor-type receptive field as derived by mutual information maximization.

Two-dimensional receptive fields are investigated from an information theoretic viewpoint. It is known that the spatially localized and orientation- and spatial-frequency-tuned receptive fields of simple cells in the visual cortex are well described by Gabor functions. This paper shows that the Gabor functions are derived as solutions for a certain mutual-information maximization problem. This means that, in a low signal-to-noise ratio limit, the Gabor-type receptive field can extract the maximum information from input local images with regard to their categories. Accordingly, this suggests that the receptive fields of simple cells are optimally designed from an information theoretic viewpoint.

Journal Article↗
PubMed2006

Using mutual information to measure the impact of multiple genetic factors on AIDS.

Since the discovery of the 32-base-pair deletion in the CCR5 chemokine receptor gene (CCR5-Delta32) and its effect on HIV-1 infection and AIDS progression, many genetic factors affecting AIDS have been identified. Here we quantify the impact of 13 of these factors on AIDS progression using a new statistic based on the mutual information between causal factors and disease, the explained fraction. The influence of causal factors on disease is commonly measured by the attributable fraction statistic, but the attributable fraction is a poor measure of the extent to which a factor explains disease because it considers only whether a factor is necessary, not whether it is sufficient. The definition of the explained fraction, which is analogous to R or the explained variation for regression models, extends naturally to multiple factor levels. Because the explained fraction is approximately additive, it can be used to estimate how much of epidemiological data is explained by known genetic or environmental factors, and conversely how much is yet to be explained by unknown factors. We show that 13 genetic factors can cumulatively explain 9% of slow progression to AIDS, an effect comparable to the effect of smoking on lung cancer.

Acquired Immunodeficiency Syndrome↗
PubMed2004

Adaptive stochastic resonance in noisy neurons based on mutual information.

Noise can improve how memoryless neurons process signals and maximize their throughput information. Such favorable use of noise is the so-called "stochastic resonance" or SR effect at the level of threshold neurons and continuous neurons. This paper presents theoretical and simulation evidence that 1) lone noisy threshold and continuous neurons exhibit the SR effect in terms of the mutual information between random input and output sequences, 2) a new statistically robust learning law can find this entropy-optimal noise level, and 3) the adaptive SR effect is robust against highly impulsive noise with infinite variance. Histograms estimate the relevant probability density functions at each learning iteration. A theorem shows that almost all noise probability density functions produce some SR effect in threshold neurons even if the noise is impulsive and has infinite variance. The optimal noise level in threshold neurons also behaves nonlinearly as the input signal amplitude increases. Simulations further show that the SR effect persists for several sigmoidal neurons and for Gaussian radial-basis-function neurons.

Algorithms↗
PubMed2006

The effects of total sleep deprivation on brain functional organization: mutual information analysis of waking human EEG.

Sleep deprivation can affect the waking electroencephalogram (EEG) that may reflect functional organization of the brain. We examined the effect of total sleep deprivation (TSD) on functional organization between different cortical areas from the waking EEG. Waking EEG data were recorded from 18 healthy male volunteers with eyes closed after 8-h night's sleep and after 24 h of TSD. The averaged cross mutual information (A-CMI) after 24 h of TSD were compared to before TSD. 24 h of TSD yielded the decreased A-CMIs in the inter-hemispheric C3-F4, C3-F8, and C3-C4 pairs: therefore, the electrodes that contribute to pairs with significant decrease of A-CMI were C3, F4, F8, and C4. The decreased A-CMIs between C3 and right frontal and central brain areas after 24 h of TSD may reflect the changes of cortico-cortical functional organization by homeostatic process during TSD. Our results of the frontal-area-related A-CMI decreases may support that the frontal brain regions are related to the homeostatic deterioration of brain function due to TSD.

Adult↗
PubMed2004

Affine and polynomial mutual information coregistration for artifact elimination in diffusion tensor imaging of newborns.

We have investigated the use of two different image coregistration algorithms for identifying local regions of erroneously high fractional anisotropy (FA) as derived from diffusion tensor imaging (DTI) data sets in newborns. The first algorithm uses conventional affine registration of each of the diffusion-weighted images to the unweighted (b = 0) image for each slice, while the second algorithm uses second-order polynomial warping. Similarity between images was determined using the mutual information (MI) criterion, which is the preferred 'cost' criterion for coregistration of images with significantly different image intensity distributions. We have found that subtle differences exist in the FA values resulting from affine and second-order polynomial coregistration and demonstrate that nonlinear distortions introduce artifacts of spatial extent similar to real white matter structures in the newborn subcortex. We show that polynomial coregistration systematically reduces the presence of erroneous regions of high FA and that such artifacts can be identified by visual inspection of FA maps resulting from affine and polynomial coregistrations. Furthermore, we show that nonlinear distortions may be particularly pronounced when acquiring image slices of axial orientation at the height of the nasal cavity. Finally, we show that third-order polynomial MI coregistration (using the images resulting from second-order coregistration as input) has no observable effect on the resulting FA maps.

Algorithms↗
PubMed2004

Clinical validation of the normalized mutual information method for registration of CT and MR images in radiotherapy of brain tumors.

Image registration integrates information of different imaging modalities and has the potential to improve target volume determination in radiotherapy planning. This paper describes the implementation and validation of a 3D fully automated registration procedure in the process of radiotherapy treatment planning of brain tumors. 15 Patients with various brain tumors received CT and MR brain imaging before the start of radiotherapy. The normalized mutual information (NMI) method was used for image registration. Registration accuracy was estimated by performing statistical analysis of coordinate differences between CT and MR anatomical landmarks along the x-, y- and z-axes. Second, a visual validation protocol was developed to validate the quality of individual registration solutions and this protocol was tested in a series of 36 CT-MR registration procedures with intentionally applied registration errors. The mean coordinate differences between CT and MR landmarks along the x- and y-axes were in general within 0.5 mm. The mean coordinate differences along the z-axis were within 1.0 mm, which is of the same magnitude as the applied slice thickness in scanning. Second, the detection of intentionally applied registration errors by employment of a standardized visual validation protocol resulted in low false-negative and low false-positive rates. Application of the NMI method for the brain results in excellent automatic registration accuracy and the method has been incorporated in daily routine within our institute. A standardized validation protocol is proposed that ensures the quality of individual registrations by detecting registration errors with high sensitivity and specificity. This protocol is proposed for the validation of other linear registration methods.

Algorithms↗
PubMed2005

Species-specific analysis of protein sequence motifs using mutual information.

BACKGROUND: Protein sequence motifs are by definition short fragments of conserved amino acids, often associated with a specific function. Accordingly protein sequence profiles derived from multiple sequence alignments provide an alternative description of functional motifs characterizing families of related sequences. Such profiles conveniently reflect functional necessities by pointing out proximity at conserved sequence positions as well as depicting distances at variable positions. Discovering significant conservation characteristics within the variable positions of profiles mirrors group-specific and, in particular, evolutionary features of the underlying sequences. RESULTS: We describe the tool PROfile analysis based on Mutual Information (PROMI) that enables comparative analysis of user-classified protein sequences. PROMI is implemented as a web service using Perl and R as well as other publicly available packages and tools on the server-side. On the client-side platform-independence is achieved by generally applied internet delivery standards. As one possible application analysis of the zinc finger C2H2-type protein domain is introduced to illustrate the functionality of the tool. CONCLUSION: The web service PROMI should assist researchers to detect evolutionary correlations in protein profiles of defined biological sequences. It is available at http://promi.mpimp-golm.mpg.de where additional documentation can be found.

Amino Acid Motifs↗
PubMed2004

Mutual information based CT registration of the lung at exhale and inhale breathing states using thin-plate splines.

The advent of dynamic radiotherapy modeling and treatment techniques requires an infrastructure to weigh the merits of various interventions (breath holding, gating, tracking). The creation of treatment planning models that account for motion and deformation can allow the relative worth of such techniques to be evaluated. In order to develop a treatment planning model of a moving and deforming organ such as the lung, registration tools that account for deformation are required. We tested the accuracy of a mutual information based image registration tool using thin-plate splines driven by the selection of control points and iterative alignment according to a simplex algorithm. Eleven patients each had sequential CT scans at breath-held normal inhale and exhale states. The exhale right lung was segmented from CT and served as the reference model. For each patient, thirty control points were used to align the inhale CT right lung to the exhale CT right lung. Alignment accuracy (the standard deviation of the difference in the actual and predicted inhale position) was determined from locations of vascular and bronchial bifurcations, and found to be 1.7, 3.1, and 3.6 mm about the RL, AP, and IS directions. The alignment accuracy was significantly different from the amount of measured movement during breathing only in the AP and IS directions. The accuracy of alignment including thin-plate splines was more accurate than using affine transformations and the same iteration and scoring methodology. This technique shows promise for the future development of dynamic models of the lung for use in four-dimensional (4-D) treatment planning.

Algorithms↗
PubMed2005

A configuration space of homologous proteins conserving mutual information and allowing a phylogeny inference based on pair-wise Z-score probabilities.

BACKGROUND: Popular methods to reconstruct molecular phylogenies are based on multiple sequence alignments, in which addition or removal of data may change the resulting tree topology. We have sought a representation of homologous proteins that would conserve the information of pair-wise sequence alignments, respect probabilistic properties of Z-scores (Monte Carlo methods applied to pair-wise comparisons) and be the basis for a novel method of consistent and stable phylogenetic reconstruction. RESULTS: We have built up a spatial representation of protein sequences using concepts from particle physics (configuration space) and respecting a frame of constraints deduced from pair-wise alignment score properties in information theory. The obtained configuration space of homologous proteins (CSHP) allows the representation of real and shuffled sequences, and thereupon an expression of the TULIP theorem for Z-score probabilities. Based on the CSHP, we propose a phylogeny reconstruction using Z-scores. Deduced trees, called TULIP trees, are consistent with multiple-alignment based trees. Furthermore, the TULIP tree reconstruction method provides a solution for some previously reported incongruent results, such as the apicomplexan enolase phylogeny. CONCLUSION: The CSHP is a unified model that conserves mutual information between proteins in the way physical models conserve energy. Applications include the reconstruction of evolutionary consistent and robust trees, the topology of which is based on a spatial representation that is not reordered after addition or removal of sequences. The CSHP and its assigned phylogenetic topology, provide a powerful and easily updated representation for massive pair-wise genome comparisons based on Z-score computations.

Algorithms↗
PubMed2002

Mutual information detects a decreased interdependence between RR and SAP in orthostatic intolerance after microgravity condition.

Orthostatic intolerance is the most serious symptom of cardiovascular deconditioning induced by microgravity. However, the exact mechanisms underlying these alterations have not been completely clarified. Several methods for studying the time series of systolic arterial pressure and RR interval have been proposed both in the time and in the frequency domain. However, these methods did not produce definitive results. In fact heart rate and arterial pressure show a complex pattern of global variability which is likely due to non linear feedback which involves the autonomic nervous system and to "stochastic" influences. Aim of this study was to evaluate the degree of interdependence between the mechanisms responsible for the variability of SAP and RR signals in subjects exposed to head down (HD). This quantification was achieved by using Mutual Information (MI).

Journal Article↗
PubMed1999

Combined MEG and EEG source imaging by minimization of mutual information.

Though very frequently assumed, the necessity to operate a joint processing of simultaneous magnetoencephalography (MEG) and electroencephalography (EEG) recordings for functional brain imaging has never been clearly demonstrated. However, the very last generation of MEG instruments allows the simultaneous recording of brain magnetic fields and electrical potentials on the scalp. But the general fear regarding the fusion between MEG and EEG data is that the drawbacks from one modality will systematically spoil the performances of the other one without any consequent improvement. This is the case for instance for the estimation of deeper or radial sources with MEG. In this paper, we propose a method for a cooperative processing of MEG and EEG in a distributed source model. First, the evaluation of the respective performances of each modality for the estimation of every dipole in the source pattern is made using a conditional entropy criterion. Then, the algorithm operates a preprocessing of the MEG and EEG gain matrices which minimizes the mutual information between these two transfer functions, by a selective weighting of the MEG and EEG lead fields. This new combined EEG/MEG modality brings major improvements to the localization of active sources, together with reduced sensitivity to perturbations on data.

Bayes Theorem↗
PubMed2000

Patient set-up using portal images: 2D/2D image registration using mutual information.

OBJECTIVE: Conformal radiation therapy requires accurate patient set-up for each fraction delivery. Electronic portal imaging devices allow the acquisition of portal images just before and even during dose delivery. However, the quantitative interpretation of these images in determining and correcting the patient's position remains uncertain, and automated methods are therefore being developed. Such methods must be usable for the different radiation therapy techniques. They must be robust and as automated as possible for use in clinical routines. This work was undertaken to establish the feasibility of 2D/2D registration for portal/portal and portal/simulator images in radiotherapy. MATERIALS AND METHODS: This paper describes an automated method based on the combination of calibration algorithms and pixel-based registration algorithms. We present experiments with the different imaging techniques, some of which use a phantom with and without a gold standard. Preliminary results obtained using patient data are also presented and discussed. RESULTS: The results obtained with a phantom demonstrated that this automated method for 2D/2D registration is fast, accurate, and robust, even in the case of blurred images for small treatment fields. CONCLUSIONS: Mutual information is a feasible method for 2D/2D portal/portal and portal/simulator image registration in radiotherapy.

Algorithms↗
PubMed2003

Coupled multi-shape model and mutual information for medical image segmentation.

This paper presents extensions which improve the performance of the shape-based deformable active contour model presented earlier in [9]. In contrast to that work, the segmentation framework that we present in this paper allows multiple shapes to be segmented simultaneously in a seamless fashion. To achieve this, multiple signed distance functions are employed as the implicit representations of the multiple shape classes within the image. A parametric model for this new representation is derived by applying principal component analysis to the collection of these multiple signed distance functions. By deriving a parametric model in this manner, we obtain a coupling between the multiple shapes within the image and hence effectively capture the co-variations among the different shapes. The parameters of the multi-shape model are then calculated to minimize a single mutual information-based cost functional for image segmentation. The use of a single cost criterion further enhances the coupling between the multiple shapes as the deformation of any given shape depends, at all times, upon every other shape, regardless of their proximity. We demonstrate the utility of this algorithm to the segmentation of the prostate gland, the rectum, and the internal obturator muscles for MR-guided prostate brachytherapy.

Algorithms↗
PubMed2004

Mutual information in coupled multi-shape model for medical image segmentation.

This paper presents extensions which improve the performance of the shape-based deformable active contour model presented earlier in [IEEE Conf. Comput. Vision Pattern Recog. 1 (2001) 463] for medical image segmentation. In contrast to that previous work, the segmentation framework that we present in this paper allows multiple shapes to be segmented simultaneously in a seamless fashion. To achieve this, multiple signed distance functions are employed as the implicit representations of the multiple shape classes within the image. A parametric model for this new representation is derived by applying principal component analysis to the collection of these multiple signed distance functions. By deriving a parametric model in this manner, we obtain a coupling between the multiple shapes within the image and hence effectively capture the co-variations among the different shapes. The parameters of the multi-shape model are then calculated to minimize a single mutual information-based cost criterion for image segmentation. The use of a single cost criterion further enhances the coupling between the multiple shapes as the deformation of any given shape depends, at all times, upon every other shape, regardless of their proximity. We found that this resulting algorithm is able to effectively utilize the co-dependencies among the different shapes to aid in the segmentation process. It is able to capture a wide range of shape variability despite being a parametric shape-model. And finally, the algorithm is robust to large amounts of additive noise. We demonstrate the utility of this segmentation framework by applying it to a medical application: the segmentation of the prostate gland, the rectum, and the internal obturator muscles for MR-guided prostate brachytherapy.

Algorithms↗
PubMed2006

Dissociating episodic from semantic access mode by mutual information measures: evidence from aging and Alzheimer's disease.

Re-thinking the semantic vs. episodic distinction with new experimental paradigms, we have designed a simple classification task to assess episodic and semantic access modes to memory for famous faces. The task requires to post 54 cards into nine mail boxes arranged in a 3x3 (nationality by field of activity) array, allowing for a quantitative analysis of the distribution of their responses, in particular of their classification errors. By using an information theoretical approach, we have developed an index of the concentration of errors, i.e. the metric content index. High levels of metric content indicate strong dependence of the classification performance on perceived relations among the set of stimuli, and therefore a preferred semantic access mode. We have found (1) a significant effect of age on the metric content, indicative of a shift from episodic to semantic access in older subjects (Experiment 1); (2) a significant correlation between the metric content and relevant measures assessing episodic and semantic retrieval mode in the Remember (R)/Know (K) paradigm introduced by Tulving [Tulving, E. 1985. Memory and consciousness. Can. Psychol. 26, 1-12] (Experiment 2); (3) a significant increase in metric content in early Alzheimer's disease patients compared to normal controls, consistent with their specific impairment in episodic access (Experiment 3).

Adult↗
PubMed2005

Phase mutual information as a similarity measure for registration.

This article describes a new method for non-rigid alignment of multimodal images. Multimodal image registration is most often accomplished by modelling, in some sense, an intensity mapping between the images. Here, the alternative strategy of modelling a relationship between local image phase is introduced. This method is intrinsically image feature based, and searches for relationships between feature appearances, rather than tissue class intensity. This enables registration of modalities for which image intensity is not a simple function of tissue class, for example ultrasound. It is also demonstrated that this method performs comparably to an intensity method even when the images are related by a simple intensity transform, but that the phase method is significantly more robust to image artifacts which corrupt the ideal intensity mapping.

Algorithms↗
PubMed2006

Adaptive Markov modeling for mutual-information-based, unsupervised MRI brain-tissue classification.

This paper presents a novel method for brain-tissue classification in magnetic resonance (MR) images that relies on a very general, adaptive statistical model of image neighborhoods. The method models MR-tissue intensities as derived from stationary random fields. It models the associated Markov statistics nonparametrically via a data-driven strategy. This paper describes the essential theoretical aspects underpinning adaptive, nonparametric Markov modeling and the theory behind the consistency of such a model. This general formulation enables the method to easily adapt to various kinds of MR images and the associated acquisition artifacts. It implicitly accounts for the intensity nonuniformity and performs reasonably well on T1-weighted MR data without nonuniformity correction. The method minimizes an information-theoretic metric on the probability density functions associated with image neighborhoods to produce an optimal classification. It automatically tunes its important internal parameters based on the information content of the data. Combined with an atlas-based initialization, it is completely automatic. Experiments on real, simulated, and multimodal data demonstrate the advantages of the method over the current state-of-the-art.

Algorithms↗
PubMed1997

Mutual information analysis and detection of interictal morphological differences in interictal epileptiform discharges of patients with partial epilepsies.

The purpose of this paper is to compare the morphological features of interictal epileptiform discharges (IED) in patients with benign epilepsy of childhood with centrotemporal spikes to IED of those with symptomatic localization related epilepsies using information theory. Three patients from each clinical group were selected. Two-second epochs centered at the peak negativity of the sharp waves were analyzed from a referential montage during stage I sleep. The epochs from the two groups were compared using parametric and information theory analysis. Information analysis determined the likelihood of correctly identifying the clinical group based on the IED. Standard parametric, morphological and spectral analyses were also performed. We found no significant difference in the morphology of the sharp wave between the two groups. The after-going slow wave contained the greatest information that separated the two groups. This result was supported by morphological and spectral differences in the after-going slow wave. Greater distinguishing information is held in the after-going slow wave than the sharp wave for the identification of clinical groups. Information analysis may assist in differentiating clinical syndromes from EEG signals.

Adult↗