Image segmentation is an important step in image processing and analysis, pattern recognition, and machine vision. A few of algorithms based on level set have been proposed for image segmentation in the last twenty years. However, these methods are time consuming, and sometime fail to extract the correct regions especially for noisy images. Recently, neutrosophic set (NS) theory has been applied to image processing for noisy images with indeterminant information. In this paper, a novel image segmentation approach is proposed based on the filter in NS and level set theory. At first, the image is transformed into NS domain, which is described by three membership sets (T, I and F). Then, a filter is newly defined and employed to reduce the indeterminacy of the image. Finally, a level set algorithm is used in the image after filtering operation for image segmentation. Experiments have been conducted using different images. The results demonstrate that the proposed method can segment the images effectively and accurately. It is especially able to remove the noise effect and extract the correct regions on both the noise-free images and the images with different levels of noise.
图像分割是图像处理与分析、模式识别和机器视觉的重要步骤。近二十年来，人们提出了一些基于水平集的图像分割算法。然而，这些方法耗时长，有时无法提取出正确的区域，特别是对于有噪声的图像。近年来，neutrosophic set (NS)理论被应用于含有不确定信息的噪声图像处理中。本文提出了一种基于神经网络滤波和水平集理论的图像分割方法。首先将图像变换到NS域，用3个隶属集(T, I, F)对其进行描述，然后重新定义一个滤波器来降低图像的不确定性。最后，在滤波后的图像中使用水平集算法进行图像分割。使用不同的图像进行了实验。实验结果表明，该方法可以有效、准确地分割图像。尤其能够在无噪声图像和不同噪声水平的图像上去除噪声影响，提取出正确的区域。
{"title":"A Novel Image Segmentation Algorithm Based on Neutrosophic Filtering and Level Set","authors":"Yanhui Guo","doi":"10.5281/ZENODO.22431","DOIUrl":"https://doi.org/10.5281/ZENODO.22431","url":null,"abstract":"Image segmentation is an important step in image processing and analysis, pattern recognition, and machine vision. A few of algorithms based on level set have been proposed for image segmentation in the last twenty years. However, these methods are time consuming, and sometime fail to extract the correct regions especially for noisy images. Recently, neutrosophic set (NS) theory has been applied to image processing for noisy images with indeterminant information. In this paper, a novel image segmentation approach is proposed based on the filter in NS and level set theory. At first, the image is transformed into NS domain, which is described by three membership sets (T, I and F). Then, a filter is newly defined and employed to reduce the indeterminacy of the image. Finally, a level set algorithm is used in the image after filtering operation for image segmentation. Experiments have been conducted using different images. The results demonstrate that the proposed method can segment the images effectively and accurately. It is especially able to remove the noise effect and extract the correct regions on both the noise-free images and the images with different levels of noise.","PeriodicalId":46897,"journal":{"name":"Neutrosophic Sets and Systems","volume":"1 1","pages":"8"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71047768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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