An introduction to neural computing
An introduction to neural computing
The nature of statistical learning theory
The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Automatic Brain and Tumor Segmentation
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR images
Image and Vision Computing
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
A survey of kernel and spectral methods for clustering
Pattern Recognition
Biometric scores fusion based on total error rate minimization
Pattern Recognition
A hierarchical genetic algorithm for segmentation of multi-spectral human-brain MRI
Expert Systems with Applications: An International Journal
Performance Evaluation of Kernel Based Techniques for Brain MRI Data Classification
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 02
Likelihood Ratio-Based Biometric Score Fusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
MultiK-MHKS: A Novel Multiple Kernel Learning Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection with Kernel Class Separability
IEEE Transactions on Pattern Analysis and Machine Intelligence
Threshold-optimized decision-level fusion and its application to biometrics
Pattern Recognition
Liver registration for the follow-up of hepatic tumors
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Incremental Kernel Principal Component Analysis
IEEE Transactions on Image Processing
Entropy maximization based segmentation, transmission and Wavelet Fusion of MRI images
International Journal of Hybrid Intelligent Systems
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This paper presents a framework of a medical image analysis system for the brain tumor segmentation and the brain tumor following-up over time using multi-spectral MRI images. Brain tumors have a large diversity in shape and appearance with intensities. Multi-spectral images have the advantage in providing complementary information to resolve some ambiguities. However, they may also bring along a lot of redundant information, increasing the data processing time and segmentation errors. The challenge is how to make use of the multi-spectral images effectively. Our idea of fusing these data is to extract the most useful features to obtain the best segmentation with the least cost in time. The Support Vector Machine (SVM) classification integrated with a selection of the features in a kernel space is proposed. The selection criteria are defined by the kernel class separability. Based on this SVM classification a framework to follow up the brain tumor evolution is proposed, which consists of the following steps: (1) to learn the brain tumor and select the features from the first MRI examination of the patients; (2) to automatically segment the tumor in new data using SVM; (3) to refine the tumor contour by a region growing technique. The system has been tested on real patient images with satisfying results. The quantitative evaluations by comparing with experts' manual traces and with other approaches demonstrate the effectiveness of the proposed method.