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Performance Analysis of Speckle Reduction Filtering algorithms in B-Mode Ultrasound Images

机译:B模式超声图像中散斑减少滤波算法的性能分析

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Medical field is enriched with various imaging modalities that help experts for analyzing and diagnosis of the diseases. Among them, Computer Tomography(CT), X-rays, Magnetic Resonance Imaging(MRI), Ultrasound(US) are popular practices that have great impact on the lives. Almost all the medical images are contaminated by noise while capturing the image and they pose huge difficulty for the interpretation of the details in the images. Automating various medical image tasks including prediction, classification, segmentation of certain region of interests require preprocessing of these images. Major task in preprocessing steps are noise removal and image enhancement. Ultrasonography is a non-invasive and inexpensive technique for obtaining cross sectional views of the different organs, visualizing blood vessels, tissues and detecting abnormal masses. Speckle noise intrinsically present in ultrasound images, are multiplicative unlike additive Gaussian noise. Images corrupted with noise degrades the image quality and reduces the accuracy in their interpretation. This paper is a comparative study on various denoising techniques that reduce the speckle noise present in B-mode US images. Different image quality metrics like Speckle suppression index(SSI), Peak signal to noise ratio(PSNR), and Structural similarity index measure(SSIM) are used to effectively measure the performance of the methods discussed.
机译:医疗领域丰富了各种成像方式,帮助专家分析和诊断疾病。其中,计算机断层扫描(CT),X射线,磁共振成像(MRI),超声(美国)是对生命产生很大影响的流行实践。几乎所有医学图像都被噪声污染,同时捕获图像,并且它们对图像中的细节解释巨大困难。自动化各种医学图像任务,包括预测,分类,某些兴趣区域的分割,需要预处理这些图像。预处理步骤中的主要任务是噪声消除和图像增强。超声检查是一种非侵入性和廉价的技术,用于获得不同器官,可视化血管,组织和检测异常质量的横截面视图。在超声图像中本质上存在的斑点噪声是乘法不同的与加性高斯噪声不同。噪音损坏的图像会降低图像质量,并降低了解释中的准确性。本文是对各种去噪技术的比较研究,其减少了B模式中存在的斑点噪声。不同的图像质量指标,如散斑抑制指数(SSI),峰值信号到噪声比(PSNR),以及结构相似性指数测量(SSIM)用于有效地测量所讨论的方法的性能。

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