Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Level Set Model for Image Classification
International Journal of Computer Vision
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Flux Maximizing Geometric Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Regularized Laplacian Zero Crossings as Optimal Edge Integrators
International Journal of Computer Vision
Fast SVM Training Algorithm with Decomposition on Very Large Data Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
Journal of Cognitive Neuroscience
A fast parallel optimization for training support vector machine
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
GUEST EDITORIAL: Intelligent data analysis in medicine-Recent advances
Artificial Intelligence in Medicine
Automatic clinical image segmentation using pathological modeling, PCA and SVM
Engineering Applications of Artificial Intelligence
Feature extraction of weighted data for implicit variable selection
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Weighted feature extraction with a functional data extension
Neurocomputing
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A general automatic method for clinical image segmentation is proposed. Tailored for the clinical environment, the proposed segmentation method consists of two stages: a learning stage and a clinical segmentation stage. During the learning stage, manually chosen representative images are segmented using a variational level set method driven by a pathologically modelled energy functional. Then a window-based feature extraction is applied to the segmented images. Principal component analysis (PCA) is applied to these extracted features and the results are used to train a support vector machine (SVM) classifier. During the clinical segmentation stage, the input clinical images are classified with the trained SVM. By the proposed method, we take the strengths of both machine learning and variational level set while limiting their weaknesses to achieve automatic and fast clinical segmentation. Both chest (thoracic) computed tomography (CT) scans (2D and 3D) and dental X-rays are used to test the proposed method. Promising results are demonstrated and analyzed. The proposed method can be used during preprocessing for automatic computer aided diagnosis.