Suppressed fuzzy-soft learning vector quantization for MRI segmentation

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.

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.

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.

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.