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Fully automatic detection of deep white matter T1 hypointense lesions in multiple sclerosis

PHYSICS IN MEDICINE AND BIOLOGY 2013年第23期58卷 8323-8337页

作者： Spies, Lothar Tewes, Anja Suppa, Per Opfer, Roland Buchert, Ralph Winkler, Gerhard Raji, Alaleh Jung Diagnost GmbH Hamburg Germany Charite Dept Nucl Med Berlin Germany Neurozentrum Hamburg Hamburg Germany

A novel method is presented for fully automatic detection of candidate white matter (WM) T1 hypointense lesions in three-dimensional high-resolution T1-weighted magnetic resonance (MR) images. By definition, T1 hypointense lesions have similar intensity as gray matter (GM) and thus appear darker than surrounding normal WM in T1-weighted images. The novel method uses a standard classification algorithm to partition T1-weighted images into GM, WM and cerebrospinal fluid (CSF). As a consequence, T1 hypointense lesions are assigned an increased GM probability by the standard classification algorithm. The GM component image of a patient is then tested voxel-by-voxel against GM component images of a normative database of healthy individuals. Clusters (>= 0.1 ml) of significantly increased GM density within a predefined mask of deep WM are defined as lesions. The performance of the algorithm was assessed on voxel level by a simulation study. A maximum dice similarity coefficient of 60% was found for a typical T1 lesion pattern with contrasts ranging from WM to cortical GM, indicating substantial agreement between ground truth and automatic detection. Retrospective application to 10 patients with multiple sclerosis demonstrated that 93 out of 96 T1 hypointense lesions were detected. On average 3.6 false positive T1 hypointense lesions per patient were found. The novel method is promising to support the detection of hypointense lesions in T1-weighted images which warrants further evaluation in larger patient samples.

关键词： ground truth Lesions Patients automatic detection pixel sorting algorithm White Matter

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algorithm for Reaction Classification

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JOURNAL OF CHEMICAL INFORMATION AND MODELING 2013年第11期53卷 2884-2895页

作者： Kraut, Hans Eiblmaier, Josef Grethe, Guenter Loew, Peter Matuszczyk, Heinz Saller, Heinz InfoChem GmbH D-81241 Munich Bavaria Germany

Reaction classification has important applications, and many approaches to classification have been applied. Our own algorithm tests all maximum common substructures (MCS) between all reactant and product molecules in order to find an atom mapping containing the minimum chemical distance (MCD). Recent publications have concluded that new MCS algorithms need to be compared with existing methods in a reproducible environment, preferably on a generalized test set, yet the number of test sets available is small, and they are not truly representative of the range of reactions that occur in real reaction databases. We have designed a challenging test set of reactions and are making it publicly available and usable with InfoChem's software or other classification algorithms. We supply a representative set of example reactions, grouped into different levels of difficulty, from a large number of reaction databases that chemists actually encounter in practice, in order to demonstrate the basic requirements for a mapping algorithm to detect the reaction centers in a consistent way. We invite the scientific community to contribute to the future extension and improvement of this data set, to achieve the goal of a common standard.

关键词： algorithm reaction classification Reaction centers sorting algorithm Taxonomy REACTION MCS gene algorithms TEST SETS scientific community

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Video Behavior Analysis Using Topic Models and Rough Sets

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IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 2013年第1期8卷 56-67页

作者： Zhao, Liang Shang, Lin Gao, Yang Yang, Yubin Jia, Xiuyi Nanjing Univ Nanjing Jiangsu Peoples R China Nanjing Univ Sci & Technol Nanjing Jiangsu Peoples R China

Video behavior analysis is both meaningful and challenging in computer vision. As the monitoring equipment become readily cheaper and available, massive video data have been generated. As abnormal behaviors are rare, manual surveillance may drastically increase the chances of false positives or false negatives. It is hence useful to achieve automatic surveillance. Abnormal behaviors are usually complex and short. It is however difficult to completely list the abnormal behaviors. We can view abnormalities as outliers. Sometimes abnormalities co-occur with normalities, which cannot be distinguished in some feature space.

关键词： Analytical models Classification algorithms Computational modeling Computer vision Data models Feature extraction Rough sets Rough sets Feature extraction ANALYSIS MODELS monitoring equipment data models Eigenvector behavioral research abnormal behavior computer vision sorting algorithm Computational modeling

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Interpretation of type 2 diabetes mellitus relevant GC-MS metabolomics fingerprints by using random forests

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ANALYTICAL METHODS 2013年第18期5卷 4883-4889页

作者： Huang, Jian-Hua Xie, Hua-Lin Yan, Jun Cao, Dong-Sheng Lu, Hong-Mei Xu, Qing-Song Liang, Yi-Zeng Cent South Univ Res Ctr Modernizat Tradit Chinese Med Dept Chem & Chem Engn Changsha 410083 Hunan Peoples R China Cent South Univ Sch Math Sci & Comp Technol Changsha 410083 Hunan Peoples R China Yangtze Normal Univ Sch Chem & Chem Engn Fuling 408100 Peoples R China

In this study, we discussed the application of random forest (RF) methods for extracting relevant biological knowledge from two type 2 diabetes mellitus (T2DM) relevant metabolomics fingerprinting experiments. The models constructed by RF could visually discriminate type 2 diabetic mice from a healthy control group and represent the variance of metabolic profiles of diabetic mice in the therapeutic process with repaglinide. Simultaneously, some informative metabolites have been successfully discovered by means of variable importance ranking in the RF program. The current research demonstrated that RF was a versatile classification algorithm, which was suitable for the analysis of complex metabolomics data and would be a complement or an alternative to pathogenesis and pharmacodynamics research.

关键词： current research radiofrequency sorting algorithm Gas Chromatography-Mass Spectrometry Fingerprints Control Groups Diabetes Mellitus Metabolomics Type 2 Diabetes Mellitus pharmacodynamics interpretation

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The Uncracked Pieces in Database Cracking

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PROCEEDINGS OF THE VLDB ENDOWMENT 2013年第2期7卷 97-108页

作者： Schuhknecht, Felix Martin Jindal, Alekh Dittrich, Jens Saarland Univ Informat Syst Grp Saarbrucken Germany MIT CSAIL Cambridge MA 02139 USA

Database cracking has been an area of active research in recent years. The core idea of database cracking is to create indexes adaptively and incrementally as a side-product of query processing. Several works have proposed different cracking techniques for different aspects including updates, tuple-reconstruction, convergence, concurrency-control, and robustness. However, there is a lack of any comparative study of these different methods by an independent group. In this paper, we conduct an experimental study on database cracking. Our goal is to critically review several aspects, identify the potential, and propose promising directions in database cracking. With this study, we hope to expand the scope of database cracking and possibly leverage cracking in database engines other than MonetDB. We repeat several prior database cracking works including the core cracking algorithms as well as three other works on convergence (hybrid cracking), tuple-reconstruction (sideways cracking), and robustness (stochastic cracking) respectively. We evaluate these works and show possible directions to do even better. We further test cracking under a variety of experimental settings, including high selectivity queries, low selectivity queries, and multiple query access patterns. Finally, we compare cracking against different sorting algorithms as well as against different main-memory optimised indexes, including the recently proposed Adaptive Radix Tree (ART). Our results show that: (i) the previously proposed cracking algorithms are repeatable, (ii) there is still enough room to significantly improve the previously proposed cracking algorithms, (iii) cracking depends heavily on query selectivity, (iv) cracking needs to catch up with modern indexing trends, and (v) different indexing algorithms have different indexing signatures.

关键词： Cracks Concurrency control Database systems Indexing (of information) Query processing Stochastic systems Comparative studies Experimental settings High selectivity Indexing algorithms Multiple queries Query selectivity sorting algorithm Tuple reconstruction

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Reducing Computational Complexity of Gating Procedures Using sorting algorithms

Reducing Computational Complexity of Gating Procedures Using...

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International Conference on Information Fusion

作者： Viet Duc Nguyen Tim Claussen Digital Signal Processing and System Theory Christian-Albrechts-Universitat zu Kiel

ISBN: (纸本)9781479902842

Gating is an important part in many data association algorithms, specifically tracking algorithms. Its purpose is the preselection of suitable measurements or observations respectively in order to avoid unlikely measurement-to-track associations. As such, it can be essential for a low computational load. A very common gating method is individual ellipsoidal gating incorporating the Mahalanobis distance. On the one hand it is a reliable gating method used in many different tracking techniques, e.g., the global nearest neighbor method or the Multi-Hypothesis Tracking approach, but on the other hand it can be also very time consuming due to the inversions of covariance matrices related to all the measurement-to-track pairs. In this paper a Gating-by-sorting approach is proposed to accelerate the gating procedure significantly. It uses sorting algorithms to carry out coarse gating followed by a second ellipsoidal gating step. The gating approach is incorporated into a multi-hypothesis tracker and applied to a real radar dataset. Results are obtained by measuring the computational load within tracking processes. They show that the proposed gating algorithm outperforms classical ellipsoidal gating by orders of magnitude.

关键词： sorting algorithm complexity classes nearest neighbor algorithm Primary election data association Inversion tracking

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Contrast Research of Two Kinds of Integrated sorting algorithms

Contrast Research of Two Kinds of Integrated Sorting Algorit...

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International Conference on Materials Science and Information Technology (MSIT 2011)

作者： Li XiaoBo Weifang Univ Weifang Peoples R China

ISBN: (纸本)9783037853191

Boosting and Bagging are two kinds of important voting sorting algorithms. Boosting algorithm can generate multiple classifiers by serialization through adjustment of sample weight;Bagging can generate multiple classifiers by parallelization. Different algorithms are composed of different loss and different integration mode, through integration of Bagging and Boosting algorithm and naive Bayes algorithm, the Bagging NB and Ada Boost NB algorithms are constructed. Through experiment contrast of UCI data set, the result shows Bagging NB algorithm is relatively stable, it can produce the sorting result superior than that of NB algorithm, AdaBoost NB algorithm is greatly affected by the singular value in data distribution, the result with foundation of NB algorithm is relatively poor on part of data set, and that might have negative influence on the classifier algorithm.

关键词： sorting algorithm Bagging Ada Boost naive Bayes

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Non-Homogeneous Hidden Markov Chain Models for Wavelet-Based Hyperspectral Image Processing

Non-Homogeneous Hidden Markov Chain Models for Wavelet-Based...

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51st IEEE Annual Allerton Conference on Communication, Control, and Computing

作者： Duarte, Marco F. Parente, Mario Univ Massachusetts Dept Elect & Comp Engn Amherst MA 01003 USA

ISBN: (纸本)9781479934096

We consider the use of non-homogeneous Markov chain (NHMC) models for wavelet transformations of hyper-spectral signatures to generate features for signal processing purposes. Inspired by the use of hidden Markov trees for natural images, the NHMC model enables the characterization of absorption bands and other structural features of mineral spectra that are used by experts in tasks like classification and unmixing, primarily in an ad-hoc fashion. We show that NHMC models can successfully identify and capture the information in a spectral signature dataset that can be exploited by standard classification algorithms to identify and differentiate spectral families. We also identify several metrics that can help determine whether each spectral band is informative to classification in a multiscale fashion.

关键词： Hidden Markov models Markov chain hyperspectral imagery spectral bands Absorption bands signal processing Metronidazole Professional personnel Image processing Religious Missions Spectrum signatures isostructuralism sorting algorithm

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Continuous Authentication using Mouse Dynamics 12

Continuous Authentication using Mouse Dynamics

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12th Annual International Conference of the Biometrics-Special-Interest-Group (BIOSIG) of the Gesellschaft-fur-Informatik (GI) e V

作者： Mondal, Soumik Bours, Patrick Gjovik Univ Coll NISlab N-2820 Gjovik Norway

ISBN: (纸本)9783885796060

In this paper, we demonstrate a new way to perform continuous authentication using Mouse Dynamics as the behavioural biometric modality. In the proposed scheme, the user will be authenticated per mouse event performed on his/her system. We have used a publicly available mouse dynamics dataset and extracted per event features suitable for the proposed scheme. In this research, we have used the mouse dynamics data of 49 users and evaluated the system performance with 6 machine learning algorithms. In this approach, the genuine user has never been classified as an impostor throughout a full session whereas the average number of mouse actions an impostor could perform before detection is 94 from the best classification algorithm with a person based threshold.

关键词： Fraud Mus Mouse System performance sorting algorithm dynamics Authentication Persons Machine learning algorithms

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A Cost-sensitive Ensemble Classifier for Breast Cancer Classification

A Cost-sensitive Ensemble Classifier for Breast Cancer Class...

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8th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI)

作者： Krawczyk, Bartosz Schaefer, Gerald Wozniak, Michal Wroclaw Univ Technol Dept Syst & Comp Networks PL-50370 Wroclaw Poland Univ Loughborough Dept Comp Sci Louisville KY USA

ISBN: (纸本)9781467364003;9781467363976

Breast cancer is the most commonly diagnosed form of cancer in women. Pattern classification approaches often have difficulties with breast cancer related datasets as the available training data are typically imbalanced with many more benign cases recorded than malignant ones, leading to a bias in the classification and insufficient sensitivity. In this paper, we present an ensemble classification algorithm that addresses this problem by employing cost-sensitive decision trees as base classifiers which are trained on random feature subspaces to ensure diversity, and an evolutionary algorithm for simultaneous classifier selection and fusion. Experimental results on two different breast cancer datasets confirm our approach to work well and to provide boosted sensitivity compared to various other state-of-the-art ensembles.

关键词： Breast Cancer Classifiers sorting algorithm cancer classification evolutionary algorithm Decision Trees Dataset

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