Document Type : Original Research Article
Author
Bachelor of Dental Surgery student at Dalian Medical University, Dalian, Liaoning, China
Abstract
One of the most complicated tasks in digital image processing is image segmentation. Due to increasing attention to this technique by researchers and turning it into a vital role, it is used in many practical fields such as medical applications. Today, in modern dentistry, techniques based on the use of computers, such as planning and planning before surgery, are being developed day by day. Each of these sub-bands contains important information that can be used in image segmentation. This important information is ignored in image segmentation. The main idea is to somehow add this information to the original image. The sub-bands of wavelet coefficients are added to the first sub-band of wavelet transform coefficients, corresponding to approximation coefficients, which are closer to the original image in terms of value and appearance, using integration methods. After that, the wavelet transform image is done. In this case, the obtained image contains more information than the original image, and better and more accurate segmentation is done. In this study, the EM algorithm was used to segment the dental radiology images, and to improve this algorithm, the k-means algorithm was used for the initial estimation of the parameters of the EM algorithm. Despite its simplicity, this algorithm is considered a basic method for many other clustering methods. Morphological operators have been used to improve segmentation.
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