Utilization of image interpolation and fusion in brain tumor segmentation

• Published in: International journal for numerical methods in biomedical engineering 2021-08, Vol.37 (8), p.e3449-n/a
• Main Authors: 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.
• Format: Article
• Language: English
• Subjects: Brain; Brain cancer; brain tumor; Brain tumors; Computed tomography; Diagnosis; image fusion; image interpolation; Image quality; Image segmentation; Interpolation; Magnetic resonance imaging; MCFO; Medical imaging; Neuroimaging; Optimization; segmentation; Tumors
• ISSN: 2040-7939; 2040-7947
• DOI: 10.1002/cnm.3449

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. This paper introduces a framework for brain tumor segmentation, and then localization of the region of the tumor, clearly. The proposed framework begins with the fusion of MR and CT images by the NSST with the aid of the MCFO to get the optimum fusion result from the quality metrics perspective. After that, an image interpolation technique is employed to obtain an HR image from the available LR ones. Finally, the threshold and the watershed segmentation are applied sequentially to localize the tumor region.