Medical image segmentation by fuzzy logic techniques

Hata, Yutaka; Kobashi, Syoji; Hirano, Shoji

doi:10.1109/icsmc.1998.726731

Cited by 7 publications

(1 citation statement)

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“…In Rojas [11], a neural network method is used in order to identify different features of the radiata pine species after using a confusion matrix. The use of fuzzy logic method for image segmentation has been used in Hata et al [12]; however, there is little bibliography about the use of these methods in wood analysis. Nevertheless, both approaches have demonstrated feasibility for image segmentation in other areas.…”

Section: Introductionmentioning

confidence: 99%

Comparison analysis of nonlinear segmentation methods for selected wood features in sugar maple

Reyes

Ossandón

2012

J Wood Sci

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Two classification methods, a feed-forward neural network and a fuzzy logic algorithm, were used for the automatic identification of CT images for selected wood features in sugar maple, one of the most important hardwoods in eastern Canada. Three wood characteristics were selected for automatic identification together with the background as a default. Local features, such as position and local pixel values were used as the neural networks inputs. The fuzzy sets consisted of four different possible pixel values and four possible distances from the center of the log. The fuzzy method used in this study was of the Mamdani type. Five sugar maple logs were randomly selected for this study. One of the logs is used for the training of the neural network and the others for validation and comparison. The structure of the neural network was optimized and was used for the segmentation of the other logs. An efficiency function, consisting of the number of pixels correctly labeled, was defined for the evaluation of the segmentation process. This study shows that a segmentation based on a fuzzy method has better capabilities for generalization than one based on a feed-forward method.

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Section: Introductionmentioning

confidence: 99%