Faisal Ahmed, Parveen Sharmin, Badsha Shahriar, Sarwar Hasan, Reza Ahmed Wasif
Department of Computer System & Technology, Faculty of Computer Science & Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia,
J Med Syst. 2013 Jun;37(3):9938. doi: 10.1007/s10916-013-9938-3. Epub 2013 Mar 17.
An improved and efficient method is presented in this paper to achieve a better trade-off between noise removal and edge preservation, thereby detecting the tumor region of MRI brain images automatically. Compass operator has been used in the fourth order Partial Differential Equation (PDE) based denoising technique to preserve the anatomically significant information at the edges. A new morphological technique is also introduced for stripping skull region from the brain images, which consequently leading to the process of detecting tumor accurately. Finally, automatic seeded region growing segmentation based on an improved single seed point selection algorithm is applied to detect the tumor. The method is tested on publicly available MRI brain images and it gives an average PSNR (Peak Signal to Noise Ratio) of 36.49. The obtained results also show detection accuracy of 99.46%, which is a significant improvement than that of the existing results.
本文提出了一种改进的高效方法，以在去噪和边缘保留之间实现更好的权衡，从而自动检测MRI脑图像的肿瘤区域。罗盘算子已用于基于四阶偏微分方程（PDE）的去噪技术中，以保留边缘处具有解剖学意义的信息。还引入了一种新的形态学技术，用于从脑图像中剥离颅骨区域，从而实现准确检测肿瘤的过程。最后，基于改进的单种子点选择算法的自动种子区域生长分割用于检测肿瘤。该方法在公开可用的MRI脑图像上进行了测试，平均峰值信噪比（PSNR）为36.49。所得结果还显示检测准确率为99.46%，比现有结果有显著提高。
