Abstract
Objective: The study's goal was to diagnose the condition at an earlier stage by employing the optimization-based technique for image segmentation to find deformities in MRI and Aura images
Methods: Our methodology was based on two case studies. The diseased data set of MRI images obtained from the UCI data set and Aura images from Bio-Well were taken into consideration. Using the Relevance Feedback Mechanism (RFM), the sick images that are most pertinent are determined. The optimization-based Cuckoo Search (CS) algorithm is used to find the best features. The resulting model utilising the Truncated Gaussian Mixture Model (TGMM) is used to compare the extracted characteristics. The most relevant images are chosen based on the likely hood estimation.
Results: The suggested methodology is tested using 150 retrieved Aura images, 50 trained photos, and processing of the input image utilizing morphological techniques like dilation, erosion, opening, and closing to improve the image quality. Together with segmentation quality measurements including Global Consistency Error (GCE), Probability Random Index (PRI), and Volume of Symmetry(VOS), the results are assessed using image quality metrics such as Average Difference (AD), Maximum Difference (MD), and Image Fidelity (IF).
Conclusion: The TGMM algorithm is used to conduct the experiment. The outcomes demonstrate the effectiveness of the suggested approaches in locating various injured tissues inside medical images obtained using MRI technology as well as in locating high-intensity energy zones in which a potential deformity is associated in Aura images. The outcomes reveal a respectable recognition accuracy of about 93%.
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