NDLI: Segmenting brain tumors using alignment-based features

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Fast cross-validation of kernel Fisher discriminant classifiers

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Identifying noise in an attribute of interest

TASOM-based lip tracking using the color and geometry of the face

Face recognition using OPRA-faces

Comparing machine learning classification schemes - a GIS approach

Predicting software suitability using a Bayesian belief network

Parallel algorithm for control chart pattern recognition

Data-centric automated data mining

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Intrusion detection in wireless networks using clustering techniques with expert analysis

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Multi-label associative classification of medical documents from MEDLINE

Drug design by machine learning: ensemble learning for QSAR modeling

Sparse classifiers for Automated HeartWall Motion Abnormality Detection

Attribute selection methods comparison for classification of diffuse large B-cell lymphoma

Incorporating prior knowledge into Q-learning for drug delivery individualization

Segmenting brain tumors using alignment-based features

Censored Time Trees/spl trade/ for predicting time to PSA recurrence

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Abduction and induction for learning models of inhibition in metabolic networks

Depth of anaesthesia assessment with generative polyspectral models

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Decoupling of clustering and classification steps in a cluster-based classification

A comparison of dimensionality reduction techniques for text retrieval

Learning contextual behavior of text data

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Speeding-up text categorization in a grid computing environment

Solving the traveling salesman problem through genetic algorithms with changing crossover operators

Switching for functional localization of genetic network programming

Effective data mining by integrating genetic algorithm into the data preprocessing phase

Direct evolution of hierarchical solutions with self-emergent substructures

Self bounding genetic algorithms for machine learning

Class-dependant resampling for medical applications

Automatic defect classification using boosting

An efficient multicategory classifier based on AdaBoosting

Algorithms for parallel boosting

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2004 International Conference on Machine Learning and Applications, 2004. Proceedings.

Segmenting brain tumors using alignment-based features

Content Provider	IEEE Xplore Digital Library
Author	Schmidt, M. Levner, I. Greiner, R. Murtha, A. Bistritz, A.
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Sci., Alberta Univ., Edmonton, Alta., Canada (Schmidt, M.; Levner, I.; Greiner, R.)
Abstract	Detecting and segmenting brain tumors in magnetic resonance images (MRI) is an important but time-consuming task performed by medical experts. Automating this process is a challenging task due to the often high degree of intensity and textural similarity between normal areas and tumor areas. Several recent projects have explored ways to use an aligned spatial 'template' image to incorporate spatial anatomic information about the brain, but it is not obvious what types of aligned information should be used. This work quantitatively evaluates the performance of 4 different types of alignment-based (AB) features encoding spatial anatomic information for use in supervised pixel classification. This is the first work to (1) compare several types of AB features, (2) explore ways to combine different types of AB features, and (3) explore combining AB features with textural features in a learning framework. We considered situations where existing methods perform poorly, and found that combining textural and AB features allows a substantial performance increase, achieving segmentations that very closely resemble expert annotations.
File Size	677628
File Format	PDF
ISBN	0769524958
DOI	10.1109/ICMLA.2005.56
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-12-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Magnetic resonance imaging Biomedical applications of radiation Machine learning Systems engineering and theory Hardware Labeling Neoplasms Pixel Biomedical imaging
Content Type	Text
Resource Type	Article

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