Using ACO-based TVR-DART for 3D Image Reconstruction

Abstract

The ability to clearly see inside structures is crucial for doctors to make accurate diagnoses and provide effective treatments. A powerful 3D brain imaging was essential for the radiologist in both the diagnosis and subsequent procedures for several brain tumours. Brain tumour detection via MRI and subsequent image restoration is a computationally intensive and unpredictable process. Due to the limitations of 2D imaging, 3D tumour reconstruction is necessary for studies and therapeutic planning. Due to the tumour’s intricacy and variety, MRI imaging is often unsuccessful. Particularly in the realm of biomedical imaging, 3D image reconstruction has emerged as one of the most promising paths for the processing of digital inputs. The research resulted in a methodical and effective strategy for 3D restoration. It involves combining many processes, including picture pre-processing, image segmentation, 3D model advancement, and tumour reconstruction. In this work, we introduce the total-variation regularised discrete algebraic reconstruction technique (TVR DART) algorithm, which uses ant colony optimization (ACO) to perform reconstruction, and the modified fuzzy c means segmentation clustering (MFCM) method, which uses a supervised learning method.