Anoop B N, Joseph Justin, Williams J, Jayaraman J Sivaraman, Sebastian Ansa Maria, Sihota Praveer
School of Electronics, St. Joseph's College of Engineering & Technology, Palai, 686579, India.
Department of Biomedical Engineering, National Institute of Technology, Raipur, Chhattisgarh, 492010, India.
Australas Phys Eng Sci Med. 2018 Jun;41(2):415-427. doi: 10.1007/s13246-018-0638-7. Epub 2018 Apr 13.
Glioblastoma multiforme (GBM) appears undifferentiated and non-enhancing on magnetic resonance (MR) imagery. As MRI does not offer adequate image quality to allow visual discrimination of the boundary between GBM focus and perifocal vasogenic edema, surgical and radiotherapy planning become difficult. The presence of noise in MR images influences the computation of radiation dosage and precludes the edge based segmentation schemes in automated software for radiation treatment planning. The performance of techniques meant for simultaneous denoising and sharpening, like high boost filters, high frequency emphasize filters and two-way anisotropic diffusion is sensitive to the selection of their operational parameters. Improper selection may cause overshoot and saturation artefacts or noisy grey level transitions can be left unsuppressed. This paper is a prospective case study of the performance of high boost filters, high frequency emphasize filters and two-way anisotropic diffusion on MR images of GBM, for their ability to suppress noise from homogeneous regions and to selectively sharpen the true morphological edges. An objective method for determining the optimum value of the operational parameters of these techniques is also demonstrated. Saturation Evaluation Index (SEI), Perceptual Sharpness Index (PSI), Edge Model based Blur Metric (EMBM), Sharpness of Ridges (SOR), Structural Similarity Index Metric (SSIM), Peak Signal to Noise Ratio (PSNR) and Noise Suppression Ratio (NSR) are the objective functions used. They account for overshoot and saturation artefacts, sharpness of the image, width of salient edges (haloes), susceptibility of edge quality to noise, feature preservation and degree of noise suppression. Two-way diffusion is found to be superior to others in all these respects. The SEI, PSI, EMBM, SOR, SSIM, PSNR and NSR exhibited by two-way diffusion are 0.0016 ± 0.0012, 0.2049 ± 0.0187, 0.0905 ± 0.0408, 2.64 × 10 ± 1.6 × 10, 0.9955 ± 0.0024, 38.214 ± 5.2145 and 0.3547 ± 0.0069, respectively.
多形性胶质母细胞瘤（GBM）在磁共振（MR）图像上表现为未分化且无强化。由于MRI无法提供足够的图像质量以直观区分GBM病灶与灶周血管源性水肿之间的边界，手术和放疗计划变得困难。MR图像中的噪声会影响辐射剂量的计算，并排除了用于放射治疗计划的自动化软件中基于边缘的分割方案。诸如高增强滤波器、高频强调滤波器和双向各向异性扩散等用于同时去噪和锐化的技术性能，对其操作参数的选择很敏感。选择不当可能会导致过冲和饱和伪影，或者无法抑制噪声灰度级转换。本文是一项前瞻性案例研究，研究高增强滤波器、高频强调滤波器和双向各向异性扩散在GBM的MR图像上的性能，考察它们从均匀区域抑制噪声以及选择性锐化真实形态边缘的能力。还展示了一种确定这些技术操作参数最佳值的客观方法。使用的客观函数包括饱和评估指数（SEI）、感知锐度指数（PSI）、基于边缘模型的模糊度量（EMBM）、脊线锐度（SOR）、结构相似性指数度量（SSIM）、峰值信噪比（PSNR）和噪声抑制率（NSR）。它们考虑了过冲和饱和伪影、图像锐度、显著边缘（光晕）宽度、边缘质量对噪声的敏感性、特征保留以及噪声抑制程度。在所有这些方面，发现双向扩散优于其他方法。双向扩散表现出的SEI、PSI、EMBM、SOR、SSIM、PSNR和NSR分别为0.0016±0.0012、0.2049±0.0187、0.0905±0.0408、2.64×10±1.6×10、0.9955±0.0024、38.214±5.2145和0.3547±0.0069。
