Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
A variational level set approach to multiphase motion
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
International Journal of Computer Vision
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
Learning-based algorithm selection for image segmentation
Pattern Recognition Letters
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
Automatic clinical image segmentation using pathological modelling, PCA and SVM
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
IEEE Transactions on Image Processing
Editorial: Recent advances in data mining
Engineering Applications of Artificial Intelligence
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Due to the presence of complicated topological and residual features, the segmentation of medical imagery is a difficult problem. In this paper, an automated approach to clinical image segmentation is presented. The processing of these images in our approach is divided into learning and segmentation stages to facilitate the application of principal component analysis with a support vector machine (SVM) classifier. During the initial learning stage, representative images are chosen to represent typical input images. These images are segmented using a variational level set method driven by a modeled energy functional designed to delineate the pathological characteristics of the images. Then a window-based feature extraction is applied to these segmented images. Principal component analysis is applied to these extracted features and the results are used to train an SVM classifier. After training the SVM, any time a clinical image needs to be segmented, it is simply classified with the trained SVM. By the proposed method, we take the strengths of both machine learning and the variational level set method while limiting their weaknesses to achieve automatic and fast clinical segmentation. To test the proposed system, both chest (thoracic) computed tomography (CT) scans (2D and 3D) and dental X-rays are used. Promising results are demonstrated and analyzed. The proposed method can be used during pre-processing for automatic computer-aided diagnosis.