A Fuzzy Decision Tree Classifier for Recognition of Fuzzy Image Regions | ||||
| The International Conference on Electrical Engineering | ||||
| Article 107, Volume 6, 6th International Conference on Electrical Engineering ICEENG 2008, May 2008, Page 1-18 PDF (456.17 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/iceeng.2008.34374 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| M. Zaki1; M. Y. El Nahas1; Eraky Atta2 | ||||
| 1College of Engineering, Al-Azhar University, Cairo, Egypt. | ||||
| 2Egyptian Armed Forces. | ||||
| Abstract | ||||
| Abstract: In this paper we present a framework for image segmentation and fuzzy region recognition based on fuzzy decision tree (FDT) classifier. The inference of the fuzzy decision tree starts by construction of crisp classification and regression tree (CART), then fuzzification of decision boundary at each node. Fuzzy regions in digital images are extracted by fuzzy c- means algorithm. Then fuzzy regions are matched with fuzzy tree models constructed for sample images from the application domain. This procedure was applied on MRI sample images for identification of normal and abnormal tissues. It was also applied for diagnosis of pathological brain cases by identification of shape and tissue of fuzzy regions. Experimental results show low error rate for the cases under study. Comparison of error rates obtained by this FDT classifier and the CART classifier shows a slight difference of 0.1 % between the two classifiers. | ||||
| Keywords | ||||
| Fuzzy decision tree; fuzzy clustering; Image Segmentation; MRI images | ||||
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