Adaptive edge-based bilinear interpolation for smart healthcare

The brain tumor is a mass of abnormal cells in the brain. The use of medical imaging techniques is essential in diagnosing brain tumors. The most commonly used methods for identifying the tumor region are magnetic resonance imaging (MRI) and computed tomography (CT). The segmentation of brain tumors is critical for tumor diagnosis. This study employs an adaptive edge-based bilinear image interpolation approach to improve the quality of HR images. An edge detector detects the edge pixels whose intensity levels decrease during the interpolation process. The detected edge pixels are treated separately to enhance the edge information. Furthermore, sharpening filters eliminate any image noise, and interpolation is performed using the bilinear interpolation method to expand the size of the image by maintaining quality, and an edge-enhanced output image is supplied as a result. The output images are assessed qualitatively by measuring the Peak Signal to Noise Ratio (PSNR) values and comparing each with other benchmarking methods. This approach achieves an average PSNR of 34.797dB, outperforming conventional interpolation methods.

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