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Selective contrast enhancement of space occupying lesions in brain using vague set approach

机译:使用Vague集方法选择性增强脑占位病变

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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 interpretation.
机译:在目前的工作中,模糊集的广义概念被应用来实现针对人的部分的医学图像的更加灵活和智能的对比度增强系统。应用小波变换可获得两幅图像,一幅包含低频分量(LF),另一幅包含高频分量(HF)。通过模糊c均值聚类(FCM)技术对LF图像进行模糊处理,并使用合理的聚类数来增强近似结构。 HF图像突出了狭窄的突起和其他细微的细节,并且还通过合理数量的聚类来模糊处理。包含基于间隔的隶属函数的Vague集方法可捕获真正受影响的肿瘤区域与正常组织之间的不确定性。正确突出肿瘤后,通过逆小波变换将LF和HF图像都转换回原始分辨率。结果表明,所提出的方法可以更成功地增强图像质量,并为视觉解释提供更好的对比度。

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