Abstract: A robust nonnegative matrix factorization (RNMF) method, in which an image sample set is split into a training set and a test set. The training set and the test set are normalized to map the image data from [0, 255] to [0, 1]. The training set matrix is pretrained by RNMF for decomposition. l2,1-deep incremental nonnegative matrix factorization (l2,1-DINMF) model is construed. The l2,1-DINMF model is configured to decompose the training set matrix into l+1 factors. After the basis matrix has been updated, and the samples of the training set and samples to be recognized are projected into a feature space. Feature representations of the test set are classified by a trained SVM classifier to obtain a predicted label, and the predicted label is compared with an actual label of the test set to calculate a recognition accuracy.

Abstract: A learning-based active surface model for medical image segmentation uses a method including: (a) data generation: obtaining medical images and associated ground truths, and splitting the sample images into a training set and a testing set; (b) raw segmentation: constructing a surface initialization network, parameters of the network trained by images and labels in the training set; (c) surface initialization: segmenting the images by the surface initialization network, and generating the point cloud data as the initial surface from the segmentation; (d) fine segmentation: constructing the surface evolution network, the parameters of the network trained by the initial surface obtained in step (c); (e) surface evolution: deforming the initial surface points along the offsets to obtain the predicted surface, the offsets presenting the prediction of the surface evolution network; (f) surface reconstruction: reconstructing the 3D volumes from the set of predicted surface points set to obtain the final segmentatio

Abstract: A learning-based active surface model for medical image segmentation uses a method including: (a) data generation: obtaining medical images and associated ground truths, and splitting the sample images into a training set and a testing set; (b) raw segmentation: constructing a surface initialization network, parameters of the network trained by images and labels in the training set; (c) surface initialization: segmenting the images by the surface initialization network, and generating the point cloud data as the initial surface from the segmentation; (d) fine segmentation: constructing the surface evolution network, the parameters of the network trained by the initial surface obtained in step (c); (e) surface evolution: deforming the initial surface points along the offsets to obtain the predicted surface, the offsets presenting the prediction of the surface evolution network; (f) surface reconstruction: reconstructing the 3D volumes from the set of predicted surface points set to obtain the final segmentatio