Title
A Semi-supervised Large Margin Algorithm for White Matter Hyperintensity Segmentation.
Abstract
Precise detection and quantification of white matter hyper-intensities (WMH) is of great interest in studies of neurodegenerative diseases (NDs). In this work, we propose a novel semi-supervised large margin algorithm for the segmentation of WMH. The proposed algorithm optimizes a kernel based max-margin objective function which aims to maximize the margin averaged over inliers and outliers while exploiting a limited amount of available labelled data. We show that the learning problem can be formulated as a joint framework learning a classifier and a label assignment simultaneously, which can be solved efficiently by an iterative algorithm. We evaluate our method on a database of 280 brain Magnetic Resonance (MR) images from subjects that either suffered from subjective memory complaints or were diagnosed with NDs. The segmented WMH volumes correlate well with the standard clinical measurement (Fazekas score), and both the qualitative visualization results and quantitative correlation scores of the proposed algorithm outperform other well known methods for WMH segmentation.
Year
DOI
Venue
2016
10.1007/978-3-319-47157-0_13
Lecture Notes in Computer Science
Field
DocType
Volume
Kernel (linear algebra),Pattern recognition,Visualization,Computer science,Segmentation,Iterative method,Outlier,Algorithm,Correlation,Artificial intelligence,Classifier (linguistics),Hyperintensity
Conference
10019
ISSN
Citations 
PageRank 
0302-9743
3
0.44
References 
Authors
8
12