Artif Intell Med - Suppressed fuzzy-soft learning vector quantization for MRI segmentation.

Tópicos

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Resumo

JECTIVE: 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.

Resumo Limpo

jectiv selforgan map som competit artifici neural network unsupervis learn increas som learn effect fuzzysoft learn vector quantiz fslvq algorithm propos literatur use fuzzi function approxim later neural interact som howev comput perform fslvq still good enough especi larg data set paper propos suppress fslvq sfslvq use suppress paramet learn schema appli sfslvq mri segment compar sever exist methodsmethod materi propos sfslvq algorithm exist method fslvq general lvq revis general lvq altern lvq compar use numer data mri imag numer data generat mixtur normal distribut mri data set yearold femal patient diagnos retinoblastoma left eye congenit malign neoplasm retina frequent metastasi beyond lacrim cribrosa evalu perform algorithm two criteria accuraci comput effici usedresult compar sfslvq fslvq general lvq revis general lvq altern lvq numer result indic sfslvq algorithm better algorithm accuraci comput effici moreov propos sfslvq can reduc comput time increas accuraci compar exist method segment ophthalmolog mrisconclus propos sfslvq good competit learn algorithm suitabl segment ophthalmolog mri data set therefor sfslvq algorithm high recommend use mri segment aid support diagnos

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