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Selective Contrast Enhancement of Space Occupying Lesions in Brain using Vague Set Approach

机译:模糊套法脑占脑部占据损伤的选择性对比度

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In present work generalized notion of fuzzy sets is applied to achieve more flexible and intelligent contrast enhancement system for medical images of section of human bain. Applying wavelet transform two images are obtained, one is containing low frequency components (LF) other one is containing high frequency components (HF). LF image is fuzzified by fuzzy c-means clustering (FCM) technique with justified number of clusters to enhance the approximate structure. The HF images, which are highlighting narrow protrusions and other fine details are fuzzified with justified number of clusters also. Vague set approach containing interval based membership function captures the uncertainties between truly affected tumor region and normal tissues. After highlighting the tumors properly, both LF and HF images are transformed back to the original resolution by inverse wavelet transform. The results show that the proposed method can enhance the images more successfully and provide better contrast for visual interprtation.
机译:在目前的工作中,应用了模糊集的广义概念,以实现人类贝恩部分医学图像的更灵活和智能的对比增强系统。施加小波变换获得两个图像,一个含有低频分量(LF)其他一个,其包含高频分量(HF)。 LF图像通过模糊C-Means聚类(FCM)技术模糊化,具有正当数量的簇,以增强近似结构。突出突出突起和其他精细细节的HF图像是用截然性的簇数的模糊。含有基于间隔的隶属函数的模糊集合方法捕获了真正受影响的肿瘤区域和正常组织之间的不确定性。在适当地突出显示肿瘤之后,通过逆小波变换将LF和HF图像转换回原始分辨率。结果表明,该方法可以更成功地增强图像并提供更好的视觉诠释对比。

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