Suppressed fuzzy-soft learning vector quantization for MRI segmentation

Authors:
Wen-Liang Hung;De-Hua Chen;Miin-Shen Yang
Affiliations:
Department of Applied Mathematics, National Hsinchu University of Education, Hsin-Chu 30014, Taiwan;Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan;Department of Applied Mathematics, Chung Yuan Christian University, Chung-Li 32023, Taiwan
Venue:
Artificial Intelligence in Medicine
Year:
2011

Citing 11
Cited 3

Convergence and ordering of Kohonen's batch map

Neural Computation
Self-Organizing Maps

Self-Organizing Maps
Suppressed fuzzy c-means clustering algorithm

Pattern Recognition Letters
Characterization of empirical discrepancy evaluation measures

Pattern Recognition Letters
Parameter selection for suppressed fuzzy c-means with an application to MRI segmentation

Pattern Recognition Letters
Alternative learning vector quantization

Pattern Recognition
Competitive learning and soft competition for vector quantizerdesign

IEEE Transactions on Signal Processing
A novel kernelized fuzzy C-means algorithm with application in medical image segmentation

Artificial Intelligence in Medicine
Image compression based on fuzzy algorithms for learning vector quantization and wavelet image decomposition

IEEE Transactions on Image Processing
A methodology for constructing fuzzy algorithms for learning vector quantization

IEEE Transactions on Neural Networks
Generalized clustering networks and Kohonen's self-organizing scheme

IEEE Transactions on Neural Networks

A robust kernel-based fuzzy c-means algorithm by incorporating suppressed and magnified membership for MRI image segmentation

AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Optimal-selection-based suppressed fuzzy c-means clustering algorithm with self-tuning non local spatial information for image segmentation

Expert Systems with Applications: An International Journal
Skin cancer extraction with optimum fuzzy thresholding technique

Applied Intelligence

Quantified Score

Hi-index	0.02

Visualization

Abstract

Objective: A self-organizing map (SOM) is a competitive artificial neural network with unsupervised learning. To increase the SOM learning effect, a fuzzy-soft learning vector quantization (FSLVQ) algorithm has been proposed in the literature, using fuzzy functions to approximate lateral neural interaction of the SOM. However, the computational performance of FSLVQ is still not good enough, especially for large data sets. In this paper, we propose a suppressed FSLVQ (S-FSLVQ) using suppression with a parameter learning schema. We then apply the S-FSLVQ to MRI segmentation and compare it with several existing methods. Methods and materials: The proposed S-FSLVQ algorithm and some existing methods, such as FSLVQ, generalized LVQ, revised generalized LVQ and alternative LVQ, are compared using numerical data and MRI images. The numerical data are generated by a mixture of normal distributions. The MRI data sets are from a 2-year-old female patient who was diagnosed with retinoblastoma of her left eye, a congenital malignant neoplasm of the retina with frequent metastasis beyond the lacrimal cribrosa. To evaluate the performance of these algorithms, two criteria for accuracy and computational efficiency are used. Results: Comparing S-FSLVQ with FSLVQ, generalized LVQ, revised generalized LVQ and alternative LVQ, the numerical results indicate that the S-FSLVQ algorithm is better than the other algorithms in accuracy and computational efficiency. Moreover, the proposed S-FSLVQ can reduce the computation time and increase accuracy compared to existing methods in segmenting these ophthalmological MRIs. Conclusions: The proposed S-FSLVQ is a good competitive learning algorithm that is very suitable for segmenting the ophthalmological MRI data sets. Therefore, the S-FSLVQ algorithm is highly recommended for use in MRI segmentation as an aid for supportive diagnoses.