El-Hag Noha A, Sedik Ahmed, El-Banby Ghada M, El-Shafai Walid, Khalaf Ashraf A M, Al-Nuaimy Waleed, Abd El-Samie Fathi E, El-Hoseny Heba M
Department of Electronics and Electrical Communications, Faculty of Engineering, Minia University, Minya, Egypt.
Department of Robotics and Intelligent Machines, Faculty of Artificial Intelligent, Kafr Elsheikh University, Kafr el-Sheikh, Egypt.
Int J Numer Method Biomed Eng. 2021 Aug;37(8):e3449. doi: 10.1002/cnm.3449. Epub 2021 Jun 21.
Brain tumor is a mass of anomalous cells in the brain. Medical imagining techniques have a vital role in the diagnosis of brain tumors. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) techniques are the most popular techniques to localize the tumor area. Brain tumor segmentation is very important for the diagnosis of tumors. In this paper, we introduce a framework to perform brain tumor segmentation, and then localize the region of the tumor, accurately. The proposed framework begins with the fusion of MR and CT images by the Non-Sub-Sampled Shearlet Transform (NSST) with the aid of the Modified Central Force Optimization (MCFO) to get the optimum fusion result from the quality metrics perspective. After that, image interpolation is applied to obtain a High-Resolution (HR) image from the Low-Resolution (LR) ones. The objective of the interpolation process is to enrich the details of the fusion result prior to segmentation. Finally, the threshold and the watershed segmentation are applied sequentially to localize the tumor region, clearly. The proposed framework enhances the efficiency of segmentation to help the specialists diagnose brain tumors.
脑肿瘤是大脑中一团异常细胞。医学成像技术在脑肿瘤诊断中起着至关重要的作用。磁共振成像（MRI）和计算机断层扫描（CT）技术是定位肿瘤区域最常用的技术。脑肿瘤分割对于肿瘤诊断非常重要。在本文中，我们介绍了一个执行脑肿瘤分割的框架，然后准确地定位肿瘤区域。所提出的框架首先通过非下采样剪切波变换（NSST）并借助改进的中心力优化（MCFO）对MR图像和CT图像进行融合，以便从质量指标的角度获得最佳融合结果。之后，应用图像插值从低分辨率（LR）图像中获取高分辨率（HR）图像。插值过程的目的是在分割之前丰富融合结果的细节。最后，依次应用阈值分割和分水岭分割来清晰地定位肿瘤区域。所提出的框架提高了分割效率，有助于专家诊断脑肿瘤。
