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An improved feature based image fusion technique for enhancement of liver lesions

机译:一种改进的基于特征的图像融合技术,用于增强肝脏病变

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摘要

This paper describes two methods for enhancement of edge and texture of medical images. In the first method optimal kernel size of range filter suitable for enhancement of liver and lesions is deduced. The results have been compared with conventional edge detection algorithms. In the second method the feasibility of feature based pixel wise image fusion for enhancing abdominal images is investigated. Among the different algorithms developed in the medical image fusion pixel level fusion is capable of retaining the maximum relevant information with better implementation and computational efficiency. Conventional image fusion includes multi-modal fusion and multi-resolution fusion. The present work attempts to fuse together, texture enhanced and edge enhanced images of the input image in order to obtain significant enhancement in the output image. The algorithm is tested in low contrast medical images. The result shows an improvement in contrast and sharpness of output image which will provide a basis for a better visual interpretation leading to more accurate diagnosis. Qualitative and quantitative performance evaluation is done by calculating information entropy, MSE, PSNR, SSIM and Tenengrad values. (C) 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
机译:本文介绍了两种用于增强医学图像的边缘和纹理的方法。在第一方法中,推导出适合增强肝脏和病变的范围滤波器的最佳核尺寸。将结果与传统的边缘检测算法进行了比较。在第二种方法中,研究了用于增强腹部图像的基于特征的像素明智图像融合的可行性。在医学图像融合中开发的不同算法中,像素电平融合能够以更好的实现和计算效率保留最大相关信息。传统的图像融合包括多模态融合和多分辨率融合。目前的工作试图融合在一起,纹理增强和边缘增强图像的输入图像,以便在输出图像中获得显着的增强。该算法在低对比度医学图像中进行测试。结果表明了输出图像的对比度和清晰度的改进,这将为更好的视觉解释提供依据,这导致更准确的诊断。通过计算信息熵,MSE,PSNR,SSIM和Tenengrad值来完成定性和定量性能评估。 (c)2018年纳雷斯州博士生物庭院研究所和波兰科学院的生物医学工程。 elsevier b.v出版。保留所有权利。

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