Akciğer Bölgesinin Bölütlenmesinde Karmaşık Değerli Sınıflayıcıların Karşılaştırılması

Year 2011, Volume: 8 Issue: 1, - , 01.05.2011

Murat Ceylan , Yüksel Özbay Osman Nuri Uçan

Abstract

Image segmentation is an important step in many computer vision algorithms.
The objective of segmentation is to obtain an optimal region of convergence. Error in
this stage will impact all higher level activities. In this study, three types of complexvalued
classifier were compared to the segmentation of lung region. These classifiers are
complex-valued artificial neural network (CVANN), complex-valued wavelet artificial neural
network (CVWANN) and complex valued artificial neural network with complex wavelet
transform (CWT-CVANN). To test the performance of the proposed systems, Lung Image
Database Consortium (LIDC) dataset was used. Obtained results shown that lung region
segmentation done using CVWANN and CVANN with worst accuracy rates as 38.59% and
75.66%, respectively. On the other hand, CWT-CVANN structure segmented lung region with 100% accuracy rate. Moreover, this structure required only 4.5 second per image for
segmentation task. Thus, it is concluded that CWT-CVANN is a comprising method in
lung region segmentation problem.

Keywords

Lung region segmentation, complex wavelet transform, complex-valued artificial neural network

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