Semi-supervised discriminative classification with application to tumorous tissues segmentation of MR brain images

Song, Yangqiu; Zhang, Changshui; Lee, Jianguo; Wang, Fei; Xiang, Shiming; Zhang, Dan

doi:10.1007/s10044-008-0104-3

Cited by 24 publications

(18 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Problems where categorical labels are only partially available, or available only at higher granularity levels, are commonly addressed with the help of a semi-supervised learning approaches (Bensaid et al, 1996; Song et al, 2009). Semi-supervised analysis employs user-provided labels to bias the process of discovering structure in the data.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised cluster analysis of imaging data

Filipovych

Resnick²,

Davatzikos

2011

NeuroImage

View full text Add to dashboard Cite

In this paper, we present a semi-supervised clustering-based framework for discovering coherent subpopulations in heterogeneous image sets. Our approach involves limited supervision in the form of labeled instances from two distributions that reflect a rough guess about subspace of features that are relevant for cluster analysis. By assuming that images are defined in a common space via registration to a common template, we propose a segmentation-based method for detecting locations that signify local regional differences in the two labeled sets. A PCA model of local image appearance is then estimated at each location of interest, and ranked with respect to its relevance for clustering. We develop an incremental k-means-like algorithm that discovers novel meaningful categories in a test image set. The application of our approach in this paper is in analysis of populations of healthy older adults. We validate our approach on a synthetic dataset, as well as on a dataset of brain images of older adults. We assess our method's performance on the problem of discovering clusters of MR images of human brain, and present a cluster-based measure of pathology that reflects the deviation of a subject's MR image from normal (i.e. cognitively stable) state. We analyze the clusters' structure, and show that clustering results obtained using our approach correlate well with clinical data.

show abstract

Section: Introductionmentioning

confidence: 99%

Semi-supervised cluster analysis of imaging data

Filipovych

Resnick²,

Davatzikos

2011

NeuroImage

View full text Add to dashboard Cite

show abstract

“…The connection strength from each node j to each other node i is encoded in element w ij of a weight matrix W . Often, a Gaussian function between points is used to specify the connection strength [25]:…”

Section: Graph-based Semi-supervised Learning (Ssl)mentioning

confidence: 99%

Sharpened graph ensemble for semi-supervised learning

Choi

Park

Shin

2013

IDA

View full text Add to dashboard Cite

The generalization ability of a machine learning algorithm varies on the specified values to the model parameters and the degree of noise in the learning dataset. If the dataset has an enough amount of labeled data points, the optimal value for the model parameter can be found via validation by using a subset of the given dataset. However, for semi-supervised learningone of the most recent learning algorithms, this is not as available as in conventional supervised learning. In semi-supervised learning, it is assumed that the dataset is given with only a few labeled data points. Therefore, holding out some of labeled data points for validation is not easy. The lack of labeled data points, furthermore, makes it difficult to estimate the degree of noise in the dataset. To circumvent the addressed difficulties, we propose to employ ensemble learning and graph sharpening. The former replaces the model parameter selection procedure to an ensemble network of the committee members trained with various values of model parameter. The latter, on the other hand, improves the performance of algorithms by removing unhelpful information caused by noise. The experimental results demonstrate the applicability of the proposed method for many real-world problems with no concern for the technical difficulties, by selecting the best parameter values and mitigating the influence of noise.

show abstract

“…Recently, a number of techniques for semisupervised medical image segmentation, especially brain image, have been suggested . Song et al presented a semisupervised method for tumorous tissues segmentation of MR brain images using nonparametric Bayesian Gaussian random field . Portela et al proposed a semisupervised clustering approach for MR brain segmentation using Gaussian Mixture Model (GMM) where the author had suggested the improvement of the segmentation results by exploring incomplete training data sets .…”

Section: Introductionmentioning

confidence: 99%

A novel automated magnetic resonance image segmentation approach based on elliptical gamma mixture model for breast lumps detection

Biswas

Ghosh

Ghosh³

2019

Int J Imaging Syst Tech

View full text Add to dashboard Cite

This article introduces a novel semisupervised automated segmentation approach for breast magnetic resonance (MR) image on multicore CPU‐GPU systems. The basic idea of the proposed method is clustering‐based semisupervised classifier devised by elliptical gamma mixture model (EGMM). Parameters of EGMM are identified by the iterative log‐expectation maximization (EM) algorithm. The suggested classifier labels the groups of voxels in an input image first and then classifies the image slices using the EGMM. Two different implementations of the proposed algorithm have been developed based on two different types of high‐performance computing architectures such as graphics processing units (GPUs) and multicore processors. To realize the real‐time segmentation performance of our algorithm with two distinctive architecture, we have tested a set of breast MR images collected from MedPix. Comparison between two architectures in terms of segmentation performance and computational cost is assessed by the analysis of simulation and experimental results.

show abstract

Semi-supervised discriminative classification with application to tumorous tissues segmentation of MR brain images

Cited by 24 publications

References 49 publications

Semi-supervised cluster analysis of imaging data

Semi-supervised cluster analysis of imaging data

Sharpened graph ensemble for semi-supervised learning

A novel automated magnetic resonance image segmentation approach based on elliptical gamma mixture model for breast lumps detection

Contact Info

Product

Resources

About