<![CDATA[This study addresses the limitations of traditional single-modality imaging techniques, such as optical microscopy, in effectively analyzing adipose tissue cells. A novel multimodal approach is introduced to overcome these challenges, combining MRI, CT, and microscopy to provide a more comprehensive and precise dataset. The system automates image processing, utilizing advanced segmentation methods to detect adipose cells more accurately while calculating cell dimensions and total image area. The results indicate that the maximum observed cell diameter reaches 10,466.64 µm, with a minimum diameter of 0.40 µm and an average diameter of 2,398.31 µm across the sample images. All measurements achieved 0% mean square error (MSE), highlighting the precision of the method. Comparative analysis reveals significant improvements in accuracy for both cell detection and quantification, outperforming conventional methods. Graphical representations further validate the reliability of this multimodal approach, demonstrating its capacity to capture intricate details of cellular structures. This innovative method holds considerable promise for enhancing medical diagnostics, particularly in metabolic disorders like obesity and diabetes, where adipose tissue plays a pivotal role. Integrating multiple imaging modalities offers a powerful tool for more informed clinical decisions, potentially leading to improved patient outcomes.]]>
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