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Potential Of Applying The Radial Basis Function

651 words - 3 pages

Potential of applying the
radial basis function (RBF) neural network architecture for the
classification of biological microscopic images displaying lung tissue
sections with idiopathic pulmonary fibrosis. For the development
of the RBF classifiers, the fuzzy means clustering algorithm
is utilized. This method is based on a fuzzy partition of the input
space and requires only a short amount of time to select both the
structure and the parameters of the RBF classifier. The new technique
was applied in lung sections acquired using a microscope
and captured by a digital camera, at a magnification of 4×. Ageand
sex-matched, 6- to 8-week-old mice (five for each time point
and five as control) were used for the induction of pulmonary fibrosis
(cf. bleomycin). Bleomycin administration initially induces lung
inflammation that is followed by a progressive destruction of the
normal lung architecture. The captured images correspond to 7, 15,
and 23 days after bleomycin or saline injection and bronchoalveolar
lavage (BAL) has been performed to the mice sample. The
images were analyzed and color features were extracted. A support
vector machines (SVMs)-based classifier was also employed
for the same problem. The resulting scores derived by visual assessment
of the images by expert pathologists were compared with
the RBF and SVM classification outcome. Overall, the RBF neural
network had a slightly better performance than that of the
SVM classifier, but both performed very well, matching to a great
percentage the scoring of the experts. There are some erroneous
predictions of the algorithm for the regions characterized as “ill”
regions (i.e., some bronchia were wrongly classified as fibrotic areas);
however, in general, the algorithm worked pretty fine in distinguishing
pathologic fromnormal inmost cases and for heterogeneous
fibrotic foci, achieving high values in terms of specificity and
Index Terms—Image analysis, interstitial pulmonary fibrosis,
neural networks, quantitative assessment of microscopic images,
quantitative phenotypic classification, support vector machines.
Manuscript received October 18, 2005; revised June 4, 2006 and September
21, 2006.
I. Maglogiannis...

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