Comparison of supervised MRI segmentation methods for tumor volume determination during therapy

Vaidyanathan, M.; Clarke, Laurence P.; Velthuizen, R.P.; Phuphanich, Surasak; Bensaid, A.; Hall, Lawrence O.; Bezdek, James C.; Greenberg, Harvey; Trotti, A.; Silbiger, Martin L.

doi:10.1016/0730-725x(95)00012-6

Cited by 88 publications

(31 citation statements)

References 21 publications

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“…In 1995, Vaiddynathan et al [9], compared two supervised multispectral classification methods: k nearest neighbour (kNN) and spectral fuzzy C-means (FCM). For these two classification approaches, nine tissue classes were considered (background, CSF, WM, GM, fat, muscle, tumor, edema, necrosis).…”

Section: Statistical Approachesmentioning

confidence: 99%

“…In 2004, Mazzara et al [18], compared the kNN approach from [9] and the KG-based approach from [10] for growth tumor volume (GTV) measurements on eleven patients with high and low-grade gliomas. As used in oncology radiation therapy, GTV corresponded to the area enclosing several contiguous clusters of enhancing pixels (i.e.…”

Section: Statistical Approachesmentioning

confidence: 99%

“…In Fletcher-Heath et al [17], in Vaiddynathan et al [9] in Droske et al [8] and in Prastawa et al [21], ground truth was defined from manual tracing from one expert.…”

Section: Deterministic Ground Truthmentioning

confidence: 99%

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Glioma Dynamics and Computational Models: A Review of Segmentation, Registration, and In Silico Growth Algorithms and their Clinical Applications

Angelini¹,

Clatz²,

Mandonnet³

et al. 2007

CMIR

103

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Tracking gliomas dynamics on MRI has became more and more important for therapeutic management. Powerful computational tools have been recently developed in this context enabling in silico growth on a virtual brain that can be matched with real 3D segmented evolution through registration between atlases and patient brain MRI data. In this paper, we provide an extensive review of existing algorithms for the three computational tasks involved in patient-specific tumor modeling: image segmentation, image registration, and in silico growth modelling (with special emphasis on the proliferation-diffusion model). Accuracy and limits of the reviewed algorithms are systematically discussed. Finally applications of these methods for both clinical practice and fundamental research are also discussed.

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Section: Statistical Approachesmentioning

confidence: 99%

Section: Statistical Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Glioma Dynamics and Computational Models: A Review of Segmentation, Registration, and In Silico Growth Algorithms and their Clinical Applications

Angelini¹,

Clatz²,

Mandonnet³

et al. 2007

CMIR

103

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show abstract

“…During the past many researchers in the field of medical imaging and soft computing have made significant survey in the field of image segmentation [1,,7,17,25]. Several authors suggested various algorithms for segmentation [15,3,23,27]. Suchendra et al, Multiscale image segmentation using a hierarchical self-organizing map [30 ] .…”

Section: Som and Hsom Image Segementationmentioning

confidence: 99%

An Enhanced Implementation of Brain Tumor Detection Using Segmentation Based on Soft Computing

Logeswari¹,

Karnan²

2010

IJCTE

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Abstract-Image Segmentation is an important and challenging factor in the medical image segmentation. This paper describes segmentation method consisting of two phases. In the first phase, the MRI brain image is acquired from patients database, In that film artifact and noise are removed. After that Hierarchical Self Organizing Map (HSOM) is applied for image segmentation. The HSOM is the extension of the conventional self organizing map used to classify the image row by row. In this lowest level of weight vector, a higher value of tumor pixels, computation speed is achieved by the HSOM with vector quantization.

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“…A number of studies have demonstrated the superiority of KNN over the Parzen Window method. 8,25 Therefore, in these studies we utilized the KNN technique. However, the proposed method is equally applicable to the Parzen Window technique with appropriate modifications.…”

Section: Introductionmentioning

confidence: 99%

Implementation of High-Dimensional Feature Map for Segmentation of MR Images

Sajja²,

Narayana

2005

Ann Biomed Eng

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A method that considerably reduces the computational and memory complexities associated with the generation of high dimensional (≥3) feature maps for image segmentation is described. The method is based on the K-nearest neighbor (KNN) classification and consists of two parts: preprocessing of feature space and fast KNN. This technique is implemented on a PC and applied for generating threeand four-dimensional feature maps for segmenting MR brain images of multiple sclerosis patients.

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Comparison of supervised MRI segmentation methods for tumor volume determination during therapy

Cited by 88 publications

References 21 publications

Glioma Dynamics and Computational Models: A Review of Segmentation, Registration, and In Silico Growth Algorithms and their Clinical Applications

Glioma Dynamics and Computational Models: A Review of Segmentation, Registration, and In Silico Growth Algorithms and their Clinical Applications

An Enhanced Implementation of Brain Tumor Detection Using Segmentation Based on Soft Computing

Implementation of High-Dimensional Feature Map for Segmentation of MR Images

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