Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis

Images acquired during free breathing using first-pass gadolinium-enhanced myocardial perfusion magnetic resonance imaging (MRI) exhibit a quasiperiodic motion pattern that needs to be compensated for if a further automatic analysis of the perfusion is to be executed. In this work, we present a method to compensate this movement by combining independent component analysis (ICA) and image registration: First, we use ICA and a time?frequency analysis to identify the motion and separate it from the intensity change induced by the contrast agent. Then, synthetic reference images are created by recombining all the independent components but the one related to the motion. Therefore, the resulting image series does not exhibit motion and its images have intensities similar to those of their original counterparts. Motion compensation is then achieved by using a multi-pass image registration procedure. We tested our method on 39 image series acquired from 13 patients, covering the basal, mid and apical areas of the left heart ventricle and consisting of 58 perfusion images each. We validated our method by comparing manually tracked intensity profiles of the myocardial sections to automatically generated ones before and after registration of 13 patient data sets (39 distinct slices). We compared linear, non-linear, and combined ICA based registration approaches and previously published motion compensation schemes. Considering run-time and accuracy, a two-step ICA based motion compensation scheme that first optimizes a translation and then for non-linear transformation performed best and achieves registration of the whole series in 32 ± 12 s on a recent workstation. The proposed scheme improves the Pearsons correlation coefficient between manually and automatically obtained time?intensity curves from .84 ± .19 before registration to .96 ± .06 after registration

Independent component analysis for domain independent watermarking

A new principled domain independent watermarking framework is presented. The new approach is based on embedding the message in statistically independent sources of the covertext to mimimise covertext distortion, maximise the information embedding rate and improve the method's robustness against various attacks. Experiments comparing the performance of the new approach, on several standard attacks show the current proposed approach to be competitive with other state of the art domain-specific methods.

Watermarking of audio signals using independent component analysis

A novel approach to watermarking of audio signals using Independent Component Analysis (ICA) is proposed. It exploits the statistical independence of components obtained by practical ICA algorithms to provide a robust watermarking scheme with high information rate and low distortion. Numerical simulations have been performed on audio signals, showing good robustness of the watermark against common attacks with unnoticeable distortion, even for high information rates. An important aspect of the method is its domain independence: it can be used to hide information in other types of data, with minor technical adaptations.

Independent component analysis and feature extraction of financial time series

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

Accounting for microsaccadic artifacts in the EEG using independent component analysis and beamforming

© 2015 Society for Psychophysiological Research. The authors would like to thank Renate Zahn and Karolin Meiß for their assistance conducting the recordings. This work was funded by the Deutsche Forschungsgemeinschaft (German Research Foundation; DFG), grant number MU 972/16-1.

On independent component analysis applied to unmixing hyperspectral data

One of the most challenging task underlying many hyperspectral imagery applications is the spectral unmixing, which decomposes a mixed pixel into a collection of reectance spectra, called endmember signatures, and their corresponding fractional abundances. Independent Component Analysis (ICA) have recently been proposed as a tool to unmix hyperspectral data. The basic goal of ICA is to nd a linear transformation to recover independent sources (abundance fractions) given only sensor observations that are unknown linear mixtures of the unobserved independent sources. In hyperspectral imagery the sum of abundance fractions associated to each pixel is constant due to physical constraints in the data acquisition process. Thus, sources cannot be independent. This paper address hyperspectral data source dependence and its impact on ICA performance. The study consider simulated and real data. In simulated scenarios hyperspectral observations are described by a generative model that takes into account the degradation mechanisms normally found in hyperspectral applications. We conclude that ICA does not unmix correctly all sources. This conclusion is based on the a study of the mutual information. Nevertheless, some sources might be well separated mainly if the number of sources is large and the signal-to-noise ratio (SNR) is high.

A study on independent component analysis over galois fields

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2015.

State estimation schemes for independent component coupled hidden Markov models

Conventional Hidden Markov models generally consist of a Markov chain observed through a linear map corrupted by additive noise. This general class of model has enjoyed a huge and diverse range of applications, for example, speech processing, biomedical signal processing and more recently quantitative finance. However, a lesser known extension of this general class of model is the so-called Factorial Hidden Markov Model (FHMM). FHMMs also have diverse applications, notably in machine learning, artificial intelligence and speech recognition [13, 17]. FHMMs extend the usual class of HMMs, by supposing the partially observed state process is a finite collection of distinct Markov chains, either statistically independent or dependent. There is also considerable current activity in applying collections of partially observed Markov chains to complex action recognition problems, see, for example, [6]. In this article we consider the Maximum Likelihood (ML) parameter estimation problem for FHMMs. Much of the extant literature concerning this problem presents parameter estimation schemes based on full data log-likelihood EM algorithms. This approach can be slow to converge and often imposes heavy demands on computer memory. The latter point is particularly relevant for the class of FHMMs where state space dimensions are relatively large. The contribution in this article is to develop new recursive formulae for a filter-based EM algorithm that can be implemented online. Our new formulae are equivalent ML estimators, however, these formulae are purely recursive and so, significantly reduce numerical complexity and memory requirements. A computer simulation is included to demonstrate the performance of our results. © Taylor & Francis Group, LLC.

Genetic parameters and principal component analysis for egg production from White Leghorn hens

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Brain areas involved in medial temporal lobe seizures: a principal component analysis of ictal SPECT data

The study describes brain areas involved in medial temporal lobe (mTL) seizures of 12 patients. All patients showed so-called oro-alimentary behavior within the first 20 s of clinical seizure manifestation characteristic of mTL seizures. Single photon emission computed tomography (SPECT) images of regional cerebral blood flow (rCBF) were acquired from the patients in ictal and interictal phases and from normal volunteers. Image analysis employed categorical comparisons with statistical parametric mapping and principal component analysis (PCA) to assess functional connectivity. PCA supplemented the findings of the categorical analysis by decomposing the covariance matrix containing images of patients and healthy subjects into distinct component images of independent variance, including areas not identified by the categorical analysis. Two principal components (PCs) discriminated the subject groups: patients with right or left mTL seizures and normal volunteers, indicating distinct neuronal networks implicated by the seizure. Both PCs were correlated with seizure duration, one positively and the other negatively, confirming their physiological significance. The independence of the two PCs yielded a clear clustering of subject groups. The local pattern within the temporal lobe describes critical relay nodes which are the counterpart of oro-alimentary behavior: (1) right mesial temporal zone and ipsilateral anterior insula in right mTL seizures, and (2) temporal poles on both sides that are densely interconnected by the anterior commissure. Regions remote from the temporal lobe may be related to seizure propagation and include positively and negatively loaded areas. These patterns, the covarying areas of the temporal pole and occipito-basal visual association cortices, for example, are related to known anatomic paths.

Quantitative component analysis of the coarse fraction of surface sediments from the Persian Gulf

In the Persian Gulf and the Gulf of Oman marl forms the primary sediment cover, particularly on the Iranian side. A detailed quantitative description of the sediment components > 63 µ has been attempted in order to establish the regional distribution of the most important constituents as well as the criteria governing marl sedimentation in general. During the course of the analysis, the sand fraction from about 160 bottom-surface samples was split into 5 phi° fractions and 500 to 800 grains were counted in each individual fraction. The grains were cataloged in up to 40 grain type catagories. The gravel fraction was counted separately and the values calculated as weight percent. Basic for understanding the mode of formation of the marl sediment is the "rule" of independent availability of component groups. It states that the sedimentation of different component groups takes place independently, and that variation in the quantity of one component is independent of the presence or absence of other components. This means, for example, that different grain size spectrums are not necessarily developed through transport sorting. In the Persian Gulf they are more likely the result of differences in the amount of clay-rich fine sediment brought in to the restricted mouth areas of the Iranian rivers. These local increases in clayey sediment dilute the autochthonous, for the most part carbonate, coarse fraction. This also explains the frequent facies changes from carbonate to clayey marl. The main constituent groups of the coarse fraction are faecal pellets and lumps, the non carbonate mineral components, the Pleistocene relict sediment, the benthonic biogene components and the plankton. Faecal pellets and lumps are formed through grain size transformation of fine sediment. Higher percentages of these components can be correlated to large amounts of fine sediment and organic C. No discernable change takes place in carbonate minerals as a result of digestion and faecal pellet formation. The non-carbonate sand components originate from several unrelated sources and can be distinguished by their different grain size spectrum; as well as by other characteristics. The Iranian rivers supply the greatest amounts (well sorted fine sand). Their quantitative variations can be used to trace fine sediment transport directions. Similar mineral maxima in the sediment of the Gulf of Oman mark the path of the Persian Gulf outflow water. Far out from the coast, the basin bottoms in places contain abundant relict minerals (poorly sorted medium sand) and localized areas of reworked salt dome material (medium sand to gravel). Wind transport produces only a minimal "background value" of mineral components (very fine sand). Biogenic and non-biogenic relict sediments can be placed in separate component groups with the help of several petrographic criteria. Part of the relict sediment (well sorted fine sand) is allochthonous and was derived from the terrigenous sediment of river mouths. The main part (coarse, poorly sorted sediment), however, was derived from the late Pleistocene and forms a quasi-autochthonous cover over wide areas which receive little recent sedimentation. Bioturbation results in a mixing of the relict sediment with the overlying younger sediment. Resulting vertical sediment displacement of more than 2.5 m has been observed. This vertical mixing of relict sediment is also partially responsible for the present day grain size anomalies (coarse sediment in deep water) found in the Persian Gulf. The mainly aragonitic components forming the relict sediment show a finely subdivided facies pattern reflecting the paleogeography of carbonate tidal flats dating from the post Pleistocene transgression. Standstill periods are reflected at 110 -125m (shelf break), 64-61 m and 53-41 m (e.g. coare grained quartz and oolite concentrations), and at 25-30m. Comparing these depths to similar occurrences on other shelf regions (e. g. Timor Sea) leads to the conclusion that at this time minimal tectonic activity was taking place in the Persian Gulf. The Pleistocene climate, as evidenced by the absence of Iranian river sediment, was probably drier than the present day Persian Gulf climate. Foremost among the benthonic biogene components are the foraminifera and mollusks. When a ratio is set up between the two, it can be seen that each group is very sensitive to bottom type, i.e., the production of benthonic mollusca increases when a stable (hard) bottom is present whereas the foraminifera favour a soft bottom. In this way, regardless of the grain size, areas with high and low rates of recent sedimentation can be sharply defined. The almost complete absence of mollusks in water deeper than 200 to 300 m gives a rough sedimentologic water depth indicator. The sum of the benthonic foraminifera and mollusca was used as a relative constant reference value for the investigation of many other sediment components. The ratio between arenaceous foraminifera and those with carbonate shells shows a direct relationship to the amount of coarse grained material in the sediment as the frequence of arenaceous foraminifera depends heavily on the availability of sand grains. The nearness of "open" coasts (Iranian river mouths) is directly reflected in the high percentage of plant remains, and indirectly by the increased numbers of ostracods and vertebrates. Plant fragments do not reach their ultimate point of deposition in a free swimming state, but are transported along with the remainder of the terrigenous fine sediment. The echinoderms (mainly echinoids in the West Basin and ophiuroids in the Central Basin) attain their maximum development at the greatest depth reached by the action of the largest waves. This depth varies, depending on the exposure of the slope to the waves, between 12 to 14 and 30 to 35 m. Corals and bryozoans have proved to be good indicators of stable unchanging bottom conditions. Although bryozoans and alcyonarian spiculae are independent of water depth, scleractinians thrive only above 25 to 30 m. The beginning of recent reef growth (restricted by low winter temperatures) was seen only in one single area - on a shoal under 16 m of water. The coarse plankton fraction was studied primarily through the use of a plankton-benthos ratio. The increase in planktonic foraminifera with increasing water depth is here heavily masked by the "Adjacent sea effect" of the Persian Gulf: for the most part the foraminifera have drifted in from the Gulf of Oman. In contrast, the planktonic mollusks are able to colonize the entire Persian Gulf water body. Their amount in the plankton-benthos ratio always increases with water depth and thereby gives a reliable picture of local water depth variations. This holds true to a depth of around 400 m (corresponding to 80-90 % plankton). This water depth effect can be removed by graphical analysis, allowing the percentage of planktonic mollusks per total sample to be used as a reference base for relative sedimentation rate (sedimentation index). These values vary between 1 and > 1000 and thereby agree well with all the other lines of evidence. The "pteropod ooze" facies is then markedly dependent on the sedimentation rate and can theoretically develop at any depth greater than 65 m (proven at 80 m). It should certainly no longer be thought of as "deep sea" sediment. Based on the component distribution diagrams, grain size and carbonate content, the sediments of the Persian Gulf and the Gulf of Oman can be grouped into 5 provisional facies divisions (Chapt.19). Particularly noteworthy among these are first, the fine grained clayey marl facies occupying the 9 narrow outflow areas of rivers, and second, the coarse grained, high-carbonate marl facies rich in relict sediment which covers wide sediment-poor areas of the basin bottoms. Sediment transport is for the most part restricted to grain sizes < 150 µ and in shallow water is largely coast-parallel due to wave action at times supplemented by tidal currents. Below the wave base gravity transport prevails. The only current capable of moving sediment is the Persian Gulf outflow water in the Gulf of Oman.

Application of Independent Components Analysis with the JADE algorithm and NIR hyperspectral imaging for revealing food adulteration

In recent years, Independent Components Analysis (ICA) has proven itself to be a powerful signal-processing technique for solving the Blind-Source Separation (BSS) problems in different scientific domains. In the present work, an application of ICA for processing NIR hyperspectral images to detect traces of peanut in wheat flour is presented. Processing was performed without a priori knowledge of the chemical composition of the two food materials. The aim was to extract the source signals of the different chemical components from the initial data set and to use them in order to determine the distribution of peanut traces in the hyperspectral images. To determine the optimal number of independent component to be extracted, the Random ICA by blocks method was used. This method is based on the repeated calculation of several models using an increasing number of independent components after randomly segmenting the matrix data into two blocks and then calculating the correlations between the signals extracted from the two blocks. The extracted ICA signals were interpreted and their ability to classify peanut and wheat flour was studied. Finally, all the extracted ICs were used to construct a single synthetic signal that could be used directly with the hyperspectral images to enhance the contrast between the peanut and the wheat flours in a real multi-use industrial environment. Furthermore, feature extraction methods (connected components labelling algorithm followed by flood fill method to extract object contours) were applied in order to target the spatial location of the presence of peanut traces. A good visualization of the distributions of peanut traces was thus obtained

Combined analysis of categorical and numerical descriptors of Australian groundnut accessions using nonlinear principal component analysis

For users of germplasm collections, the purpose of measuring characterization and evaluation descriptors, and subsequently using statistical methodology to summarize the data, is not only to interpret the relationships between the descriptors, but also to characterize the differences and similarities between accessions in relation to their phenotypic variability for each of the measured descriptors. The set of descriptors for the accessions of most germplasm collections consists of both numerical and categorical descriptors. This poses problems for a combined analysis of all descriptors because few statistical techniques deal with mixtures of measurement types. In this article, nonlinear principal component analysis was used to analyze the descriptors of the accessions in the Australian groundnut collection. It was demonstrated that the nonlinear variant of ordinary principal component analysis is an appropriate analytical tool because subspecies and botanical varieties could be identified on the basis of the analysis and characterized in terms of all descriptors. Moreover, outlying accessions could be easily spotted and their characteristics established. The statistical results and their interpretations provide users with a more efficient way to identify accessions of potential relevance for their plant improvement programs and encourage and improve the usefulness and utilization of germplasm collections.