Abstract
The signal-to-noise ratio (SNR) is an important measure of the quality of computed tomography (CT) images. In this study, a new clustering method is proposed to calculate the SNR ratio of CT image. Multi- Objective Simulated Annealing clustering is used for the comparison based on segmentation parameters such as SNR ratio. Two samples are used in this study as phantom materials, namely, Rhizophora Spp. binderless and araldite resin particleboard, with dimension of 20 cm x 20 cm. For each scanned datum, ImageJ software is utilised as the combination method to analyse CT images. Results shows that the automatic clustering algorithm improves the SNR results of the sample images. In addition, the SNR value of images using MOSA clustering is higher than that of normal CT images.
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