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Dive into the research topics where Amine M. Bensaid is active.

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IEEE Transactions on Neural Networks | 1992

A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain

Lawrence O. Hall; Amine M. Bensaid; Laurence P. Clarke; Robert P. Velthuizen; Martin S. Silbiger; James C. Bezdek

Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms, and a supervised computational neural network. Initial clinical results are presented on normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. For a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed, with fuzz-c-means approaches being slightly preferred over feedforward cascade correlation results. Various facets of both approaches, such as supervised versus unsupervised learning, time complexity, and utility for the diagnostic process, are compared.


Magnetic Resonance Imaging | 1995

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

M. Vaidyanathan; Laurence P. Clarke; Robert P. Velthuizen; S. Phuphanich; Amine M. Bensaid; Lawrence O. Hall; James C. Bezdek; Harvey Greenberg; A. Trotti; Martin S. Silbiger

Two different multispectral pattern recognition methods are used to segment magnetic resonance images (MRI) of the brain for quantitative estimation of tumor volume and volume changes with therapy. A supervised k-nearest neighbor (kNN) rule and a semi-supervised fuzzy c-means (SFCM) method are used to segment MRI slice data. Tumor volumes as determined by the kNN and SFCM segmentation methods are compared with two reference methods, based on image grey scale, as a basis for an estimation of ground truth, namely: (a) a commonly used seed growing method that is applied to the contrast enhanced T1-weighted image, and (b) a manual segmentation method using a custom-designed graphical user interface applied to the same raw image (T1-weighted) dataset. Emphasis is placed on measurement of intra and inter observer reproducibility using the proposed methods. Intra- and interobserver variation for the kNN method was 9% and 5%, respectively. The results for the SFCM method was a little better at 6% and 4%, respectively. For the seed growing method, the intra-observer variation was 6% and the interobserver variation was 17%, significantly larger when compared with the multispectral methods. The absolute tumor volume determined by the multispectral segmentation methods was consistently smaller than that observed for the reference methods. The results of this study are found to be very patient case-dependent. The results for SFCM suggest that it should be useful for relative measurements of tumor volume during therapy, but further studies are required. This work demonstrates the need for minimally supervised or unsupervised methods for tumor volume measurements.


world congress on computational intelligence | 1994

Genetic algorithm guided clustering

James C. Bezdek; Srinivas Boggavarapu; Lawrence O. Hall; Amine M. Bensaid

Genetic algorithms provide an approach to optimization. Unsupervised clustering algorithms attempt to optimize the placement of like objects into homogeneous classes or clusters. We describe an approach to using genetic algorithms to optimize the clusters created during unsupervised clustering. Hard partitions of the feature space are the members of the population. They evolve into better partitions based upon the fitness function which is a version of the hard c-means optimization function. The methods of crossover and mutation are described. An example of the clustering performance of this approach is shown with the Iris data. The genetic guided clustering is shown to outperform hard c-means on the Iris data in terms of the number of patterns which are correctly placed into a partition whose majority class is the same as the assigned pattern.<<ETX>>


north american fuzzy information processing society | 1994

Genetic fuzzy clustering

Lawrence O. Hall; J.C. Bezdek; S. Boggavarpu; Amine M. Bensaid

This paper describes a genetic guided fuzzy clustering algorithm. The fuzzy-c-means functional J/sub m/ is used as the fitness function. In two domains the approach is shown to avoid some higher values of J/sub m/ to which the fuzzy-c-means algorithm will converge under some initializations. Hence, the genetic guided approach shows promise as a clustering tool.<<ETX>>


Medical Imaging 1994: Image Processing | 1994

Fuzzy cluster validity in magnetic resonance images

Amine M. Bensaid; Lawrence O. Hall; James C. Bezdek; Laurence P. Clarke

Individual cluster validation has not received as much attention as partition validation. This paper presents two measures for evaluating individual clusters in a fuzzy partition. They both account for properties of the fuzzy memberships as well as the structure of the data. The first measure is a ratio between compactness and separation of the fuzzy clusters; the second is based on counting a contradiction between properties of the fuzzy memberships and the stucture of the data. These two measures are applied and compared in evaluating fuzzy clusters generated by the fuzzy c-means algorithm for segmentation of magnetic resonance images of the brain.


Proceedings of SPIE | 1992

Partially supervised fuzzy c-means algorithm for segmentation of MR images

Amine M. Bensaid; James C. Bezdek; Lawrence O. Hall; Robert P. Velthuizen; Laurence P. Clarke

Partial supervision is introduced to the unsupervised fuzzy c-means algorithm (FCM). The resulting algorithm is called semi-supervised fuzzy c-means (SFCM). Labeled data are used as training information to improve FCMs performance. Training data are represented as training columns in SFCMs membership matrix (U), and are allowed to affect the cluster center computations. The degree of supervision is monitored by choosing the number of copies of the training set to be used in SFCM. Preliminary results of SFCM (applied to MRI segmentation) suggest that FCM finds the clusters of most interest to the user very accurately when training data is used to guide it.


Medical Imaging VI: Image Processing | 1992

Comparison of supervised pattern recognition techniques and unsupervised methods for MRI segmentation

Laurence P. Clarke; Robert P. Velthuizen; Lawrence O. Hall; James C. Bezdek; Amine M. Bensaid; Martin L. Silbiger

The use of image intensity based segmentation techniques are proposed to improve MRI contrast and provide greater confidence levels in 3-D visualization of pathology. Pattern recognition methods are proposed using both supervised and unsupervised methods. This paper emphasizes the practical problems in the selection of training data sets for supervised methods that result in instability in segmentation. An unsupervised method, namely fuzzy c- means, that does not require training data sets and produces comparable results is proposed.


IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology | 1993

Unsupervised fuzzy segmentation of 3D magnetic resonance brain images

Robert P. Velthuizen; Lawrence O. Hall; Laurence P. Clarke; Amine M. Bensaid; John A. Arrington; Martin L. Silbiger

Unsupervised fuzzy methods are proposed for segmentation of 3D Magnetic Resonance images of the brain. Fuzzy c-means (FCM) has shown promising results for segmentation of single slices. FCM has been investigated for volume segmentations, both by combining results of single slices and by segmenting the full volume. Different strategies and initializations have been tried. In particular, two approaches have been used: (1) a method by which, iteratively, the furthest sample is split off to form a new cluster center, and (2) the traditional FCM in which the membership grade matrix is initialized in some way. Results have been compared with volume segmentations by k-means and with two supervised methods, k-nearest neighbors and region growing. Results of individual segmentations are presented as well as comparisons on the application of the different methods to a number of tumor patient data sets.


Journal of Magnetic Resonance Imaging | 1995

Unsupervised Measurement of Brain Tumor Volume on MR Images

Robert P. Velthuizen; Laurence P. Clarke; Surasak Phuphanich; Lawrence O. Hall; Amine M. Bensaid; John A. Arrington; Harvey Greenberg; Martin L. Silbiger


international conference of the ieee engineering in medicine and biology society | 1991

Mri Segmentation Using Supervised And Unsupervised Methods

Amine M. Bensaid; Lawrence O. Hall; Laurence P. Clarke; Robert P. Velthuizen

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Lawrence O. Hall

University of South Florida

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Laurence P. Clarke

University of South Florida

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Martin L. Silbiger

University of South Florida

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John A. Arrington

University of South Florida

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Harvey Greenberg

University of South Florida

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Martin S. Silbiger

University of South Florida

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J.C. Bezdek

University of South Florida

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M. Vaidyanathan

University of South Florida

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