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Atlas selection plays an important role in multiatlas based image segmentation. In atlas selection methods, manifold learning based techniques have recently emerged as very promisingly. However, due to the complexity of anatomical structures in raw images, it is difficult to get accurate atlas selection results by measuring only the distance between raw images on the manifolds. In this paper, we tackle...
Non-negative matrix factorization (NMF) approximates a non-negative matrix by the product of two low-rank matrices and achieves good performance in clustering. Recently, semi-supervised NMF (SS-NMF) further improves the performance by incorporating part of the labels of few samples into NMF. In this paper, we proposed a novel graph based SS-NMF (GSS-NMF). For each sample, GSS-NMF minimizes its distances...
Graphs play a role in many semi-supervised learning algorithms, where unlabeled samples are used to find useful structural properties in the data. Dimensionality reduction and regularization based on preserving smoothness over a graph are common in these settings, and they perform particularly well if proximity in the original feature space closely reflects similarity in the classification problem...
Comparing different shapes is a fundamental problem in Computational Anatomy (CA), where a rigorous and intrinsic distance metric is key for a shape analysis system to work effectively and consistently. In this paper, we propose a shape comparison and classification framework that consists of two major components. A meridian-based shape representation, stemmed from spectral graph theory, possesses...
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