Abstract
In medical images, exist often a lot of noise, the noise will seriously affect the accuracy of the segmentation results. The traditional standard fuzzy c-means(FCM) algorithm in image segmentation do not taken into account the relationship the adjacent pixels, which leads to the standard fuzzy c-means(FCM) algorithm is very sensitive to noise in the image. Proposed improvedfuzzy c-means(FCM) algorithm, taking both the local and non-local information into the standard fuzzy c-means(FCM) clustering algorithm. The ex-periment results can show that the improved algorithm can achieve better effect than other methods of brain tissue segmentation.
Highlights
Image segmentation is the key technology in image processing and analysis .In the medical field, with the imaging technology development and medical imaging application success in the clinical, image segmentation is playing an increasingly larger role
The traditional standard fuzzy c-means(FCM) algorithm in image segmentation do not taken into account the relationship the adjacent pixels, which leads to the standard fuzzy c-means(FCM) algorithm is very sensitive to noise in the image
On the MRI brain images in the white matter (WM), brain gray matter (GM) and cerebrospinal fluid (CSF) such as the organizational structure of the correct segmentation in medical applications is of great significance
Summary
Image segmentation is the key technology in image processing and analysis .In the medical field, with the imaging technology development and medical imaging application success in the clinical, image segmentation is playing an increasingly larger role. Non-local means algorithm (NL Means), who by Buades and other peoples as image denoising algorithm first proposed [1,2] This algorithm attempts to use the image height of the redundant information to complete the work of digital image denoising, in other words, for each pixel in the image, we can find a lot of images with which had the same structure of adjacent domains samples, we are dealing with these redundant pixels to be weighted average of the sample. In order to protect the image of the fine structure and details of the information and made them not be destroyed, in the use of non-local means algorithm at the same time, local information should be considered
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