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As a parent compound of Li-rich electrodes, Li2MnO3 exhibits high capacity during the initial charge; however, it suffers notoriously low Coulombic efficiency due to oxygen and surface activities. Here, we successfully optimize the oxygen activities toward reversible oxygen redox reactions by intentionally introducing protons into lithium octahedral vacancies in the Li2MnO3 system with its original structural integrity maintained. Combining structural probes, theoretical calculations, and resonant inelastic X-ray scattering results, a moderate coupling between the introduced protons and lattice oxygen at the oxidized state is revealed, which stabilizes the oxygen activities during charging. Such a coupling leads to an unprecedented initial Coulombic efficiency (99.2%) with a greatly improved discharge capacity of 302 mAh g-1 in the protonated Li2MnO3 electrodes. These findings directly demonstrate an effective concept for controlling oxygen activities in Li-rich systems, which is critical for developing high-energy cathodes in batteries.

Redox (reduction and oxidation) chemistry provides the fundamental basis for numerous energy-related electrochemical devices. Detecting the electrochemical redox chemistry is pivotal but challenging because it requires independent probes of the cationic and anionic redox states at different electrochemical states. The synchrotron-based soft X-ray mapping of resonant inelastic X-ray scattering (mRIXS) has recently emerged as a powerful tool for exploring such states in electrochemical devices, especially batteries. High-efficiency mRIXS covers the energy range of the absorption edge with the extra dimension of information on the emitted photon energies. In this frontier article, we review recent representative demonstrations of utilizing soft X-ray mRIXS for detecting the novel chemical state during electrochemical operation and for quantifying the cationic redox reactions through inverse partial fluorescence yield analysis (mRIXS-iPFY). More importantly, the non-divalent states of oxygen in electrodes involving oxygen redox reactions could be reliably captured by mRIXS, with its reversibility quantified by the intensity variation of the characteristic mRIXS feature through a super-partial fluorescence yield analysis (mRIXS-sPFY). These recent demonstrations inspire future perspectives on using mRIXS for studying the complex phenomena in energy materials, with both technical and scientific challenges in RIXS theory, in situ/operando experiments, and spatially resolved RIXS imaging.

As a parent compound of Li-rich electrodes, Li2MnO3 exhibits high capacity during the initial charge; however, it suffers notoriously low Coulombic efficiency due to oxygen and surface activities. Here, we successfully optimize the oxygen activities toward reversible oxygen redox reactions by intentionally introducing protons into lithium octahedral vacancies in the Li2MnO3 system with its original structural integrity maintained. Combining structural probes, theoretical calculations, and resonant inelastic X-ray scattering results, a moderate coupling between the introduced protons and lattice oxygen at the oxidized state is revealed, which stabilizes the oxygen activities during charging. Such a coupling leads to an unprecedented initial Coulombic efficiency (99.2%) with a greatly improved discharge capacity of 302 mAh g-1 in the protonated Li2MnO3 electrodes. These findings directly demonstrate an effective concept for controlling oxygen activities in Li-rich systems, which is critical for developing high-energy cathodes in batteries.

Redox (reduction and oxidation) chemistry provides the fundamental basis for numerous energy-related electrochemical devices. Detecting the electrochemical redox chemistry is pivotal but challenging because it requires independent probes of the cationic and anionic redox states at different electrochemical states. The synchrotron-based soft X-ray mapping of resonant inelastic X-ray scattering (mRIXS) has recently emerged as a powerful tool for exploring such states in electrochemical devices, especially batteries. High-efficiency mRIXS covers the energy range of the absorption edge with the extra dimension of information on the emitted photon energies. In this frontier article, we review recent representative demonstrations of utilizing soft X-ray mRIXS for detecting the novel chemical state during electrochemical operation and for quantifying the cationic redox reactions through inverse partial fluorescence yield analysis (mRIXS-iPFY). More importantly, the non-divalent states of oxygen in electrodes involving oxygen redox reactions could be reliably captured by mRIXS, with its reversibility quantified by the intensity variation of the characteristic mRIXS feature through a super-partial fluorescence yield analysis (mRIXS-sPFY). These recent demonstrations inspire future perspectives on using mRIXS for studying the complex phenomena in energy materials, with both technical and scientific challenges in RIXS theory, in situ/operando experiments, and spatially resolved RIXS imaging.

Layered Li-rich Ni, Mn, Co (NMC) oxide cathodes in Li-ion batteries provide high specific capacities (>250 mAh/g) via O-redox at high voltages. However, associated high-voltage interfacial degradation processes require strategies for effective electrode surface passivation. Here, we show that an acidic surface treatment of a Li-rich NMC layered oxide cathode material leads to a substantial suppression of CO2 and O2 evolution, ∼90% and ∼100% respectively, during the first charge up to 4.8 V vs Li+/0. CO2 suppression is related to Li2CO3 removal as well as effective surface passivation against electrolyte degradation. This treatment does not result in any loss of discharge capacity and provides superior long-term cycling and rate performance in comparison to as-received, untreated materials. We also quantify the extent of lattice oxygen participation in charge compensation ("O-redox") during Li+ removal by a novel ex situ acid titration. Our results indicate that the peroxo-like species resulting from O-redox originate on the surface at least 300 mV earlier than the activation plateau region at around 4.5 V. X-ray photoelectron spectra and Mn L-edge X-ray absorption spectra of the cathode powders reveal a Li+ deficiency and a partial reduction of Mn ions on the surface of the acid-treated material. More interestingly, although the irreversible oxygen evolution is greatly suppressed through the surface treatment, O K-edge resonant inelastic X-ray scattering shows that the lattice O-redox behavior is largely sustained. The acidic treatment, therefore, only optimizes the surface of the Li-rich material and almost eliminates the irreversible gas evolution, leading to improved cycling and rate performance. This work therefore presents a simple yet effective approach to passivate cathode surfaces against interfacial instabilities during high-voltage battery operation.

Layered Li-rich Ni, Mn, Co (NMC) oxide cathodes in Li-ion batteries provide high specific capacities (>250 mAh/g) via O-redox at high voltages. However, associated high-voltage interfacial degradation processes require strategies for effective electrode surface passivation. Here, we show that an acidic surface treatment of a Li-rich NMC layered oxide cathode material leads to a substantial suppression of CO2 and O2 evolution, ∼90% and ∼100% respectively, during the first charge up to 4.8 V vs Li+/0. CO2 suppression is related to Li2CO3 removal as well as effective surface passivation against electrolyte degradation. This treatment does not result in any loss of discharge capacity and provides superior long-term cycling and rate performance in comparison to as-received, untreated materials. We also quantify the extent of lattice oxygen participation in charge compensation ("O-redox") during Li+ removal by a novel ex situ acid titration. Our results indicate that the peroxo-like species resulting from O-redox originate on the surface at least 300 mV earlier than the activation plateau region at around 4.5 V. X-ray photoelectron spectra and Mn L-edge X-ray absorption spectra of the cathode powders reveal a Li+ deficiency and a partial reduction of Mn ions on the surface of the acid-treated material. More interestingly, although the irreversible oxygen evolution is greatly suppressed through the surface treatment, O K-edge resonant inelastic X-ray scattering shows that the lattice O-redox behavior is largely sustained. The acidic treatment, therefore, only optimizes the surface of the Li-rich material and almost eliminates the irreversible gas evolution, leading to improved cycling and rate performance. This work therefore presents a simple yet effective approach to passivate cathode surfaces against interfacial instabilities during high-voltage battery operation.

Layered Li-rich Ni, Mn, Co (NMC) oxide cathodes in Li-ion batteries provide high specific capacities (>250 mAh/g) via O-redox at high voltages. However, associated high-voltage interfacial degradation processes require strategies for effective electrode surface passivation. Here, we show that an acidic surface treatment of a Li-rich NMC layered oxide cathode material leads to a substantial suppression of CO2 and O2 evolution, ~90% and ~100% respectively, during the first charge up to 4.8 V vs. Li+/0. CO2 suppression is related to Li2CO3 removal as well as effective surface passivation against electrolyte degradation. This treatment does not result in any loss of discharge capacity and provides superior long-term cycling and rate performance compared to as-received, untreated materials. We also quantify the extent of lattice oxygen participation in charge compensation (“O-redox”) during Li+ removal by a novel ex-situ acid titration. Our results indicate that the peroxo-like species resulting from O-redox originate on the surface at least 300 mV earlier than the activation plateau region around 4.5 V. X-ray photoelectron spectra and Mn-L X-ray absorption spectra of the cathode powders reveal a Li+ deficiency and a partial reduction of Mn ions on the surface of the acid-treated material. More interestingly, although the irreversible oxygen evolution is greatly suppressed through the surface treatment, our O K-edge resonant inelastic X-ray scattering shows the lattice O-redox behavior largely sustained. The acidic treatment, therefore, only optimizes the surface of the Li-rich material and almost eliminates the irreversible gas evolution, leading to improved cycling and rate performance. This work therefore presents a simple yet effective approach to passivate cathode surfaces against interfacial instabilities during high-voltage battery operation.

Layered Li-rich Ni, Mn, Co (NMC) oxide cathodes in Li-ion batteries provide high specific capacities (>250 mAh/g) via O-redox at high voltages. However, associated high-voltage interfacial degradation processes require strategies for effective electrode surface passivation. Here, we show that an acidic surface treatment of a Li-rich NMC layered oxide cathode material leads to a substantial suppression of CO2 and O2 evolution, ~90% and ~100% respectively, during the first charge up to 4.8 V vs. Li+/0. CO2 suppression is related to Li2CO3 removal as well as effective surface passivation against electrolyte degradation. This treatment does not result in any loss of discharge capacity and provides superior long-term cycling and rate performance compared to as-received, untreated materials. We also quantify the extent of lattice oxygen participation in charge compensation (“O-redox”) during Li+ removal by a novel ex-situ acid titration. Our results indicate that the peroxo-like species resulting from O-redox originate on the surface at least 300 mV earlier than the activation plateau region around 4.5 V. X-ray photoelectron spectra and Mn-L X-ray absorption spectra of the cathode powders reveal a Li+ deficiency and a partial reduction of Mn ions on the surface of the acid-treated material. More interestingly, although the irreversible oxygen evolution is greatly suppressed through the surface treatment, our O K-edge resonant inelastic X-ray scattering shows the lattice O-redox behavior largely sustained. The acidic treatment, therefore, only optimizes the surface of the Li-rich material and almost eliminates the irreversible gas evolution, leading to improved cycling and rate performance. This work therefore presents a simple yet effective approach to passivate cathode surfaces against interfacial instabilities during high-voltage battery operation.

Mutations in the Aristaless-Related Homeobox (ARX) gene cause structural anomalies of the brain, epilepsy, and neurocognitive deficits in children. During forebrain development, Arx is expressed in both pallial and subpallial progenitor cells. We previously demonstrated that elimination of Arx from subpallial-derived cortical interneurons generates an epilepsy phenotype with features overlapping those seen in patients with ARX mutations. In this report, we have selectively removed Arx from pallial progenitor cells that give rise to the cerebral cortical projection neurons. While no discernable seizure activity was recorded, these mice exhibited a peculiar constellation of behaviors. They are less anxious, less social, and more active when compared with their wild-type littermates. The overall cortical thickness was reduced, and the corpus callosum and anterior commissure were hypoplastic, consistent with a perturbation in cortical connectivity. Taken together, these data suggest that some of the structural and behavioral anomalies, common in patients with ARX mutations, are specifically due to alterations in pallial progenitor function. Furthermore, our data demonstrate that some of the neurobehavioral features found in patients with ARX mutations may not be due to on-going seizures, as is often postulated, given that epilepsy was eliminated as a confounding variable in these behavior analyses.

Object segmentation is a well-known difficult problem in pattern recognition. Until now, most of the existing object segmentation methods need to go through a time-consuming training phase prior to segmentation. Both robustness and efficiency of the existing methods have room for improvement In this work we propose a new methodology, called POSIT, for object segmentation without intensive training process. We construct a part-based shape model to substitute the training process. In the part-based framework we sequentially register object parts in the prior model to an image so that the searching space is largely reduced. Another advantage of the sequential matching is that instead of predefining the weighting parameters for the terms in the matching evaluation function, we can estimate the parameters in our model on the fly. Finally, we fine-tune the previous coarse segmentation by localized graph cuts. In the experiments, POSIT has been tested on numerous natural horse and cow images and the obtained results show the accuracy. robustness and efficiency of the proposed object segmentation method. (C) 2009 Elsevier Ltd. All rights reserved.

The aim of this work is to develop a new framework for multi-object segmentation of deep brain structures (caudate nucleus, putamen and thalamus) in medical brain images. Deep brain segmentation is difficult and challenging because the structures of interest are of relatively small size and have significant shape variations. The structure boundaries may be blurry or even missing, and the surrounding background is full of irrelevant edges. To tackle these problems, we propose a template-based framework to fuse the information of edge features, region statistics and inter-structure constraints for detecting and locating all target brain structures Such that initialization by hand is unnecessary. The multi-object template is organized in the form of a hierarchical Markov dependence tree (MDT), and multiple objects are efficiently matched to a target image by a top-to-down optimization strategy. The final segmentation is obtained through refinement by a B-spline based non-rigid registration between the exemplar image and the target image. Our approach needs only one example as training data. We have validated the proposed method on a publicly available T1-weighted magnetic resonance image database with expert-segmented brain structures. in the experiments, the proposed approach has obtained encouraging results with 0.80 Dice score for the caudate nuclei, 0.81 Dice score for the putamina and 0.84 Dice score for the thalami on average. (C) 2009 Elsevier Inc. All rights reserved.

The aim of this work is to develop a new framework for multi-object segmentation of deep brain structures (caudate nucleus, putamen and thalamus) in medical brain images. Deep brain segmentation is difficult and challenging because the structures of interest are of relatively small size and have significant shape variations. The structure boundaries may be blurry or even missing, and the surrounding background is full of irrelevant edges. To tackle these problems, we propose a template-based framework to fuse the information of edge features, region statistics and inter-structure constraints for detecting and locating all target brain structures Such that initialization by hand is unnecessary. The multi-object template is organized in the form of a hierarchical Markov dependence tree (MDT), and multiple objects are efficiently matched to a target image by a top-to-down optimization strategy. The final segmentation is obtained through refinement by a B-spline based non-rigid registration between the exemplar image and the target image. Our approach needs only one example as training data. We have validated the proposed method on a publicly available T1-weighted magnetic resonance image database with expert-segmented brain structures. in the experiments, the proposed approach has obtained encouraging results with 0.80 Dice score for the caudate nuclei, 0.81 Dice score for the putamina and 0.84 Dice score for the thalami on average. (C) 2009 Elsevier Inc. All rights reserved.

The aim of this work is to develop a new framework for multi-object segmentation of deep brain structures (caudate nucleus, putamen and thalamus) in medical brain images. Deep brain segmentation is difficult and challenging because the structures of interest are of relatively small size and have significant shape variations. The structure boundaries may be blurry or even missing, and the surrounding background is full of irrelevant edges. To tackle these problems, we propose a template-based framework to fuse the information of edge features, region statistics and inter-structure constraints for detecting and locating all target brain structures Such that initialization by hand is unnecessary. The multi-object template is organized in the form of a hierarchical Markov dependence tree (MDT), and multiple objects are efficiently matched to a target image by a top-to-down optimization strategy. The final segmentation is obtained through refinement by a B-spline based non-rigid registration between the exemplar image and the target image. Our approach needs only one example as training data. We have validated the proposed method on a publicly available T1-weighted magnetic resonance image database with expert-segmented brain structures. in the experiments, the proposed approach has obtained encouraging results with 0.80 Dice score for the caudate nuclei, 0.81 Dice score for the putamina and 0.84 Dice score for the thalami on average. (C) 2009 Elsevier Inc. All rights reserved.

We propose a new framework for multi-object segmentation of deep brain structures, which have significant shape variations and relatively small sizes in medical brain images. In the images, the structure boundaries may be blurry or even missing, and the surrounding background is a clutter and full of irrelevant edges. We suggest a template-based framework, which fuses the information of edge features, region statistics and inter-structure constraints to detect and locate all the targeted brain structures such that manual initialization is unnecessary. The multi-object template is organized in the form of a hierarchical Markov dependence tree. It makes the matching of multiple objects efficient. Our approach needs only one example as training data and alleviates the demand of a large training set. The obtained segmentation results on real data are encouraging and the proposed method enjoys several important advantages over existing methods.

Medical cerebral images can be classified to two categories: structural and functional. The former embodies anatomical structures in brains and the latter reflects the metabolic and physical characteristics of brains. The goal of segmentation of structural brain images is to divide the brain into meaningful subregions, such as tissues or structures. In this thesis, we target the problem of cerebral image segmentation and present two automatic methods on cerebral tissues and structures, respectively. The proposed method on tissue segmentation combines a label MRF with a boundary MRF and admits sophisticated prior information about boundary patterns. It considers all possible configurations of labels and edge positions in the case where a single edge goes through a local window. The prior information is general and training for different data is not needed. The proposed method on structure segmentation fuses the information of edge features, region statistics and inter-structure constraints for detecting and locating all target brain structures such that manual initialization is unnecessary. The multi-object template is organized in the form of a hierarchical Markov dependence tree, which makes the matching of multiple objects computationally efficient. The final segmentation is obtained through refinement by a B-spline based non-rigid registration between the exemplar image and the target image. Our approach needs only one example as training data and alleviates the demand of a large training set. A variety of related works on the same topics are briefly introduced and theoretical comparisons are made. Experiments have been performed on real medical data sets and the accuracy, efficiency and robustness of the proposed methods are shown in this thesis. The two proposed methods have some advantages over related works and their weaknesses are also discussed in the thesis. In the field of neuroimaging, we foresee that the research interests will be moving from low-level

We propose a new framework for multi-object segmentation of deep brain structures, which have significant shape variations and relatively small sizes in medical brain images. In the images, the structure boundaries may be blurry or even missing, and the surrounding background is a clutter and full of irrelevant edges. We suggest a template-based framework, which fuses the information of edge features, region statistics and inter-structure constraints to detect and locate all the targeted brain structures such that manual initialization is unnecessary. The multi-object template is organized in the form of a hierarchical Markov dependence tree. It makes the matching of multiple objects efficient. Our approach needs only one example as training data and alleviates the demand of a large training set. The obtained segmentation results on real data are encouraging and the proposed method enjoys several important advantages over existing methods.

Medical cerebral images can be classified to two categories: structural and functional. The former embodies anatomical structures in brains and the latter reflects the metabolic and physical characteristics of brains. The goal of segmentation of structural brain images is to divide the brain into meaningful subregions, such as tissues or structures. In this thesis, we target the problem of cerebral image segmentation and present two automatic methods on cerebral tissues and structures, respectively. The proposed method on tissue segmentation combines a label MRF with a boundary MRF and admits sophisticated prior information about boundary patterns. It considers all possible configurations of labels and edge positions in the case where a single edge goes through a local window. The prior information is general and training for different data is not needed. The proposed method on structure segmentation fuses the information of edge features, region statistics and inter-structure constraints for detecting and locating all target brain structures such that manual initialization is unnecessary. The multi-object template is organized in the form of a hierarchical Markov dependence tree, which makes the matching of multiple objects computationally efficient. The final segmentation is obtained through refinement by a B-spline based non-rigid registration between the exemplar image and the target image. Our approach needs only one example as training data and alleviates the demand of a large training set. A variety of related works on the same topics are briefly introduced and theoretical comparisons are made. Experiments have been performed on real medical data sets and the accuracy, efficiency and robustness of the proposed methods are shown in this thesis. The two proposed methods have some advantages over related works and their weaknesses are also discussed in the thesis. In the field of neuroimaging, we foresee that the research interests will be moving from low-level

Medical cerebral images can be classified to two categories: structural and functional. The former embodies anatomical structures in brains and the latter reflects the metabolic and physical characteristics of brains. The goal of segmentation of structural brain images is to divide the brain into meaningful subregions, such as tissues or structures. In this thesis, we target the problem of cerebral image segmentation and present two automatic methods on cerebral tissues and structures, respectively. The proposed method on tissue segmentation combines a label MRF with a boundary MRF and admits sophisticated prior information about boundary patterns. It considers all possible configurations of labels and edge positions in the case where a single edge goes through a local window. The prior information is general and training for different data is not needed. The proposed method on structure segmentation fuses the information of edge features, region statistics and inter-structure constraints for detecting and locating all target brain structures such that manual initialization is unnecessary. The multi-object template is organized in the form of a hierarchical Markov dependence tree, which makes the matching of multiple objects computationally efficient. The final segmentation is obtained through refinement by a B-spline based non-rigid registration between the exemplar image and the target image. Our approach needs only one example as training data and alleviates the demand of a large training set. A variety of related works on the same topics are briefly introduced and theoretical comparisons are made. Experiments have been performed on real medical data sets and the accuracy, efficiency and robustness of the proposed methods are shown in this thesis. The two proposed methods have some advantages over related works and their weaknesses are also discussed in the thesis. In the field of neuroimaging, we foresee that the research interests will be moving from low-level

We propose a new framework for multi-object segmentation of deep brain structures, which have significant shape variations and relatively small sizes in medical brain images. In the images, the structure boundaries may be blurry or even missing, and the surrounding background is a clutter and full of irrelevant edges. We suggest a template-based framework, which fuses the information of edge features, region statistics and inter-structure constraints to detect and locate all the targeted brain structures such that manual initialization is unnecessary. The multi-object template is organized in the form of a hierarchical Markov dependence tree. It makes the matching of multiple objects efficient. Our approach needs only one example as training data and alleviates the demand of a large training set. The obtained segmentation results on real data are encouraging and the proposed method enjoys several important advantages over existing methods.

This paper introduces a novel energy minimization method, namely iterated cross entropy with partition strategy (ICEPS), into the Markov random field theory. The solver, which is based on the theory of cross entropy, is general and stochastic. Unlike some popular optimization methods such as belief propagation (BP) and graph cuts (GC), ICEPS makes no assumption on the form of objective functions and thus can be applied to any type of Markov random field (MRF) models. Furthermore, compared with deterministic MRF solvers, it achieves higher performance of finding lower energies because of its stochastic property. We speed up the original cross entropy algorithm by partitioning the MRF site set and assure the effectiveness by iterating the algorithm. In the experiments, we apply ICEPS to two MRF models for medical image segmentation and show the aforementioned advantages of ICEPS over other popular solvers such as iterated conditional modes (ICM) and GC.

Markov random field (MRF) theory has been widely applied to the challenging problem of image segmentation. In this paper, we propose a new nontexture segmentation model using compound MRFs, in which the original label MRF is coupled with a new boundary MRF to help improve the segmentation performance. The boundary model is relatively general and does not need prior training on boundary patterns. Unlike some existing related work, the proposed method offers a more compact interaction between label and boundary MRFs. Furthermore, our boundary model systematically takes into account all the possible scenarios of a single edge existing in a 3 x 3 neighborhood and, thus, incorporates sophisticated prior information about the relation between label and boundary. It is experimentally shown that the proposed model can segment objects with complex boundaries and at the same time is able to work under noise corruption. The new method has been applied to medical image segmentation. Experiments on synthetic images and real clinical datasets show that the proposed model is able to produce more accurate segmentation results and satisfactorily keep the delicate boundary. It is also less sensitive to noise in both high and low signal-to-noise ratio regions than some of the existing models in common use.

Markov random field (MRF) theory has been widely applied to the challenging problem of image segmentation. In this paper, we propose a new nontexture segmentation model using compound MRFs, in which the original label MRF is coupled with a new boundary MRF to help improve the segmentation performance. The boundary model is relatively general and does not need prior training on boundary patterns. Unlike some existing related work, the proposed method offers a more compact interaction between label and boundary MRFs. Furthermore, our boundary model systematically takes into account all the possible scenarios of a single edge existing in a 3 x 3 neighborhood and, thus, incorporates sophisticated prior information about the relation between label and boundary. It is experimentally shown that the proposed model can segment objects with complex boundaries and at the same time is able to work under noise corruption. The new method has been applied to medical image segmentation. Experiments on synthetic images and real clinical datasets show that the proposed model is able to produce more accurate segmentation results and satisfactorily keep the delicate boundary. It is also less sensitive to noise in both high and low signal-to-noise ratio regions than some of the existing models in common use.

This paper introduces a novel energy minimization method, namely iterated cross entropy with partition strategy (ICEPS), into the Markov random field theory. The solver, which is based on the theory of cross entropy, is general and stochastic. Unlike some popular optimization methods such as belief propagation (BP) and graph cuts (GC), ICEPS makes no assumption on the form of objective functions and thus can be applied to any type of Markov random field (MRF) models. Furthermore, compared with deterministic MRF solvers, it achieves higher performance of finding lower energies because of its stochastic property. We speed up the original cross entropy algorithm by partitioning the MRF site set and assure the effectiveness by iterating the algorithm. In the experiments, we apply ICEPS to two MRF models for medical image segmentation and show the aforementioned advantages of ICEPS over other popular solvers such as iterated conditional modes (ICM) and GC.

This paper introduces a novel energy minimization method, namely iterated cross entropy with partition strategy (ICEPS), into the Markov random field theory. The solver, which is based on the theory of cross entropy, is general and stochastic. Unlike some popular optimization methods such as belief propagation (BP) and graph cuts (GC), ICEPS makes no assumption on the form of objective functions and thus can be applied to any type of Markov random field (MRF) models. Furthermore, compared with deterministic MRF solvers, it achieves higher performance of finding lower energies because of its stochastic property. We speed up the original cross entropy algorithm by partitioning the MRF site set and assure the effectiveness by iterating the algorithm. In the experiments, we apply ICEPS to two MRF models for medical image segmentation and show the aforementioned advantages of ICEPS over other popular solvers such as iterated conditional modes (ICM) and GC.

Markov random field (MRF) theory has been widely applied to the challenging problem of image segmentation. In this paper, we propose a new nontexture segmentation model using compound MRFs, in which the original label MRF is coupled with a new boundary MRF to help improve the segmentation performance. The boundary model is relatively general and does not need prior training on boundary patterns. Unlike some existing related work, the proposed method offers a more compact interaction between label and boundary MRFs. Furthermore, our boundary model systematically takes into account all the possible scenarios of a single edge existing in a 3 x 3 neighborhood and, thus, incorporates sophisticated prior information about the relation between label and boundary. It is experimentally shown that the proposed model can segment objects with complex boundaries and at the same time is able to work under noise corruption. The new method has been applied to medical image segmentation. Experiments on synthetic images and real clinical datasets show that the proposed model is able to produce more accurate segmentation results and satisfactorily keep the delicate boundary. It is also less sensitive to noise in both high and low signal-to-noise ratio regions than some of the existing models in common use.

To segment a whole object from an image is an essential and challenging task in image processing. In this paper, we propose a hybrid segmentation algorithm which combines prior shape information with normalized cut. With the help of shape information, we can utilize normalized cut to correctly segment the target whose boundary may be corrupted by noise or outliers. At the same time, we introduce the use of segmentation results of the normalized cut to guide the shape model, and thus avoid searching the shape space. The proposed method was demonstrated to be effective by our experiments on both synthetic and real data.

To segment a whole object from an image is an essential and challenging task in image processing. In this paper, we propose a hybrid segmentation algorithm which combines prior shape information with normalized cut. With the help of shape information, we can utilize normalized cut to correctly segment the target whose boundary may be corrupted by noise or outliers. At the same time, we introduce the use of segmentation results of the normalized cut to guide the shape model, and thus avoid searching the shape space. The proposed method was demonstrated to be effective by our experiments on both synthetic and real data.

To segment a whole object from an image is an essential and challenging task in image processing. In this paper, we propose a hybrid segmentation algorithm which combines prior shape information with normalized cut. With the help of shape information, we can utilize normalized cut to correctly segment the target whose boundary may be corrupted by noise or outliers. At the same time, we introduce the use of segmentation results of the normalized cut to guide the shape model, and thus avoid searching the shape space. The proposed method was demonstrated to be effective by our experiments on both synthetic and real data.

Markov random field (MRF) theory has widely been applied to segmentation in noisy images. This paper proposes a new MRF method. First, it couples the original labeling MRF with a boundary MRF that can help improve the performance of segmentation. Second, the boundary model is general and does not need prior training. Third, unlike existing related work, our model offers more compact interaction between the two MRFs. Experiments on synthetic images and real clinical datasets show that the proposed approach is able to produce good segmentation results, especially removing noise in low signal-to-noise ratio regions.

This paper introduces a novel solver, namely cross entropy (CE), into the MRF theory for medical image segmentation. The solver, which is based on the theory of rare event simulation, is general and stochastic. Unlike some popular optimization methods such as belief propagation and graph cuts, CE makes no assumption on the form of objective functions and thus can be applied to any type of MRF models. Furthermore, it achieves higher performance of finding more global optima because of its stochastic property. In addition, it is more efficient than other stochastic methods like simulated annealing. We tested the new solver in 4 series of segmentation experiments on synthetic and clinical, vascular and cerebral images. The experiments show that CE can give more accurate segmentation results.

Markov random field (MRF) theory has widely been applied to segmentation in noisy images. This paper proposes a new MRF method. First, it couples the original labeling MRF with a boundary MRF that can help improve the performance of segmentation. Second, the boundary model is general and does not need prior training. Third, unlike existing related work, our model offers more compact interaction between the two MRFs. Experiments on synthetic images and real clinical datasets show that the proposed approach is able to produce good segmentation results, especially removing noise in low signal-to-noise ratio regions. © 2005 IEEE.

Markov random field (MRF) theory has widely been applied to segmentation in noisy images. This paper proposes a new MRF method. First, it couples the original labeling MRF with a boundary MRF that can help improve the performance of segmentation. Second, the boundary model is general and does not need prior training. Third, unlike existing related work, our model offers more compact interaction between the two MRFs. Experiments on synthetic images and real clinical datasets show that the proposed approach is able to produce good segmentation results, especially removing noise in low signal-to-noise ratio regions. © 2005 IEEE.

Markov random field (MRF) theory has widely been applied to segmentation in noisy images. This paper proposes a new MRF method. First, it couples the original labeling MRF with a boundary MRF that can help improve the performance of segmentation. Second, the boundary model is general and does not need prior training. Third, unlike existing related work, our model offers more compact interaction between the two MRFs. Experiments on synthetic images and real clinical datasets show that the proposed approach is able to produce good segmentation results, especially removing noise in low signal-to-noise ratio regions.

This paper introduces a novel solver, namely cross entropy (CE), into the MRF theory for medical image segmentation. The solver, which is based on the theory of rare event simulation, is general and stochastic. Unlike some popular optimization methods such as belief propagation and graph cuts, CE makes no assumption on the form of objective functions and thus can be applied to any type of MRF models. Furthermore, it achieves higher performance of finding more global optima because of its stochastic property. In addition, it is more efficient than other stochastic methods like simulated annealing. We tested the new solver in 4 series of segmentation experiments on synthetic and clinical, vascular and cerebral images. The experiments show that CE can give more accurate segmentation results.

This paper introduces a novel solver, namely cross entropy (CE), into the MRF theory for medical image segmentation. The solver, which is based on the theory of rare event simulation, is general and stochastic. Unlike some popular optimization methods such as belief propagation and graph cuts, CE makes no assumption on the form of objective functions and thus can be applied to any type of MRF models. Furthermore, it achieves higher performance of finding more global optima because of its stochastic property. In addition, it is more efficient than other stochastic methods like simulated annealing. We tested the new solver in 4 series of segmentation experiments on synthetic and clinical, vascular and cerebral images. The experiments show that CE can give more accurate segmentation results.

Markov random field (MRF) theory has widely been applied to segmentation in noisy images. This paper proposes a new MRF method. First, it couples the original labeling MRF with a boundary MRF that can help improve the performance of segmentation. Second, the boundary model is general and does not need prior training. Third, unlike existing related work, our model offers more compact interaction between the two MRFs. Experiments on synthetic images and real clinical datasets show that the proposed approach is able to produce good segmentation results, especially removing noise in low signal-to-noise ratio regions. © 2005 IEEE.

Markov random field (MRF) theory has widely been applied to segmentation in noisy images. This paper proposes a new MRF method. First, it couples the original labeling MRF with a boundary MRF that can help improve the performance of segmentation. Second, the boundary model is general and does not need prior training. Third, unlike existing related work, our model offers more compact interaction between the two MRFs. Experiments on synthetic images and real clinical datasets show that the proposed approach is able to produce good segmentation results, especially removing noise in low signal-to-noise ratio regions.

The multiresolution approach is commonly used to speed up the mutual-information (MI) based registration process. Conventionally, a Gaussian pyramid is often used as a multiresolation representation. However, in multi-modal medical image registration, MI-based methods with Gaussian pyramid may suffer from the problem of short capture ranges especially at the lower resolution levels. This paper proposes a novel and straightforward multimodal image registration method based on wavelet representation, in which two matching criteria axe used including sum of difference (SAD) for improving the registration robustness and MI for assuring the registration accuracy. Experimental results show that the proposed method obtains a longer capture range than the traditional MI-based Gaussian pyramid method meanwhile maintaining comparable accuracy.

The multiresolution approach is commonly used to speed up the mutual-information (MI) based registration process. Conventionally, a Gaussian pyramid is often used as a multiresolation representation. However, in multi-modal medical image registration, MI-based methods with Gaussian pyramid may suffer from the problem of short capture ranges especially at the lower resolution levels. This paper proposes a novel and straightforward multimodal image registration method based on wavelet representation, in which two matching criteria axe used including sum of difference (SAD) for improving the registration robustness and MI for assuring the registration accuracy. Experimental results show that the proposed method obtains a longer capture range than the traditional MI-based Gaussian pyramid method meanwhile maintaining comparable accuracy.