@inproceedings {423,
	title = {Adaboost and support vector machines for white matter lesion segmentation in MR images},
	booktitle = {27th Annual International Conference of the Engineering in Medicine and Biology Society},
	year = {2005},
	abstract = {<p>The use of two powerful classification techniques (boosting and SVM) is explored for the segmentation of white-matter lesions in the MRI scans of human brain. Simple features are generated from proton density (PD) scans. Radial basis function (RBF) based Adaboost technique and support vector machines (SVM) are employed for this task. The classifiers are trained on severe, moderate and mild cases. The segmentation is performed in T1 acquisition space rather than standard space (with more slices). Hence, the proposed approach requires less time for manual verification. The results indicate that the proposed approach can handle MR field inhomogeneities quite well and is completely independent from manual selection process so that it can be run under batch mode. Segmentation performance comparison with manual detection is also provided</p>
},
	keywords = {Adaboost, biomedical MRI, brain, human brain, image classification, image segmentation, medical image processing, MR images, proton density scans, radial basis function, radial basis function networks, support vector machines, T1 acquisition, white matter lesion segmentation},
	doi = {http://dx.doi.org/10.1109/IEMBS.2005.1616447},
	author = {A Quddus and P Fieguth and O Basir}
}
@article {576,
	title = {Multiscale statistical methods for the segmentation of signals and images},
	journal = {IEEE Transactions on Image Processing},
	volume = {9},
	year = {2000},
	pages = {456 - 468},
	abstract = {<p>This paper addresses the problem of both segmenting and reconstructing a noisy signal or image. The work is motivated by large problems arising in certain scientific applications, such as medical imaging. Two objectives for a segmentation and denoising algorithm are laid out: it should be computationally efficient and capable of generating statistics for the errors in the reconstruction and estimates of the boundary locations. The starting point for the development of a suitable algorithm is a variational approach to segmentation (Shah 1992). This paper then develops a precise statistical interpretation of a one dimensional (1-D) version of this variational approach to segmentation. The 1-D algorithm that arises as a result of this analysis is computationally efficient and capable of generating error statistics. A straightforward extension of this algorithm to two dimensions would incorporate recursive procedures for computing estimates of inhomogeneous Gaussian Markov random fields. Such procedures require an unacceptably large number of operations. To meet the objective of developing a computationally efficient algorithm, the use of previously developed multiscale statistical methods is investigated. This results in the development of an algorithm for segmenting and denoising which is not only computationally efficient but also capable of generating error statistics, as desired</p>
},
	keywords = {biomedical MRI, boundary locations, brain, denoising, error statistics, Gaussian processes, image reconstruction, image segmentation, images, inhomogeneous Gaussian Markov random fields, interference suppression, Markov processes, medical image processing, medical imaging, multiscale methods, noisy image, noisy signal, reconstruction, recursive procedures, scientific applications, Segmentation, signal reconstruction, signals, statistical interpretation, variational approach, variational techniques},
	issn = {1057-7149},
	doi = {http://dx.doi.org/10.1109/83.826782},
	author = {M Schneider and P Fieguth and W C. Karl and A S. Willsky}
}
