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首页> 外文期刊>Multimedia Tools and Applications >Developing a bio-inspired multi-gene genetic programming based intelligent estimator to reduce speckle noise from ultrasound images
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Developing a bio-inspired multi-gene genetic programming based intelligent estimator to reduce speckle noise from ultrasound images

机译：开发基于生物启发的多基因遗传程序的智能估计器，以减少超声图像中的斑点噪声

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摘要

The speckle noise commonly occurs in ultrasound imaging based applications. Due to the multiplicative nature, speckle noise deteriorates the visual quality of ultrasound images. This affects the performance of radiologists and practitioners for disease diagnosis and/or patient treatment. The current study proposes a bio-inspired multi-gene genetic programming (MGGP) based intelligent estimator to reduce the speckle noise from ultrasound images. The proposed MGGP approach is based on the parallel framework of multiple genes and has effectively utilized the evolutionary learning capabilities to develop an intelligent estimator, by exploiting the useful statistical features extracted from local neighboring pixels. The performance of the proposed novel approach is evaluated on ultrasound images of common carotid artery corrupted with different noise levels. Further, the robust performance was validated on several diverse types of ultrasound images of Breast Cyst, Kidney Cancer, Liver, Liver Cyst, and Fetal Head. The proposed bio-inspired approach showed superior denoising performance over existing approaches. The proposed intelligent estimator is capable of removing speckle noise effectively while preserving the fine lines and edges. During evolution, the MGGP framework automatically selects the useful statistical features and primitive functions from a wider solution space to develop the intelligent estimator. Further, the proposed approach does not require image-dependent optimal threshold values, as conventional speckle denoising approaches required.
机译：斑点噪声通常发生在基于超声成像的应用中。由于具有乘法性质，散斑噪声会降低超声图像的视觉质量。这会影响放射科医生和从业人员进行疾病诊断和/或患者治疗的能力。当前的研究提出了一种基于生物启发的多基因遗传编程（MGGP）的智能估计器，以减少超声图像中的斑点噪声。所提出的MGGP方法基于多个基因的并行框架，并通过利用从局部相邻像素提取的有用统计特征，有效地利用了进化学习能力来开发智能估计器。所提出的新方法的性能在以不同噪声水平受损的颈总动脉超声图像上进行了评估。此外，在几种不同类型的乳腺囊肿，肾脏癌，肝，肝囊肿和胎儿头的超声图像上验证了强大的性能。拟议的生物启发方法显示出比现有方法优越的去噪性能。所提出的智能估计器能够在保留细线和边缘的同时有效消除斑点噪声。在演进过程中，MGGP框架会从更广泛的解决方案空间中自动选择有用的统计特征和原始函数，以开发智能估算器。此外，所提出的方法不需要像传统的散斑去噪方法那样依赖于图像的最佳阈值。

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