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.

中文翻译：

---

图像插值融合在脑肿瘤分割中的应用

脑肿瘤是大脑中的大量异常细胞。医学影像技术在脑肿瘤的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) 技术是最流行的定位肿瘤区域的技术。脑肿瘤分割对于肿瘤的诊断非常重要。在本文中，我们引入了一个框架来执行脑肿瘤分割，然后准确地定位肿瘤区域。所提出的框架首先通过非子采样剪切波变换（NSST）在改进的中心力优化（MCFO）的帮助下融合 MR 和 CT 图像，以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) 图像中获得高分辨率 (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，以帮助专家诊断脑肿瘤。