access icon free Efficient medical image enhancement based on CNN-FBB model

Medical image quality requirements have been increasingly stringent with the recent developments of medical technology. To meet clinical diagnosis needs, an effective medical image enhancement method based on convolutional neural networks (CNNs) and frequency band broadening (FBB) is proposed. Curvelet transform is used to deal with medical data by obtaining the curvelet coefficient in each scale and direction, and the generalised cross-validation is implemented to select the optimal threshold for performing denoising processing. Meanwhile, the cycle spinning scheme is used to wipe off the visible ringing effects along the edges of medical images. Then, FBB and a new CNN model based on the retinex model are used to improve the processed image resolution. Eventually, pixel-level fusion is made between two enhanced medical images from CNN and FBB. In the authors’ study, 50 groups of medical magnetic resonance imaging, X-ray, and computed tomography images in total have been studied. The experimental results indicate that the final enhanced image using the proposed method outperforms other methods. The resolution and the edge details of the processed image are significantly enhanced, providing a more effective and accurate basis for medical workers to diagnose diseases.

Inspec keywords: medical image processing; curvelet transforms; convolutional neural nets; image fusion; image resolution; image enhancement; biomedical MRI; computerised tomography; image denoising; diseases

Other keywords: curvelet transform; image resolution; curvelet coefficient; CNN-FBB model; convolutional neural networks; medical technology; computed tomography images; frequency band broadening; X-ray images; efficient medical image enhancement; medical magnetic resonance imaging; retinex model; visible ringing effects; medical image enhancement; medical image quality; medical data; denoising processing; cycle spinning scheme; clinical diagnosis

Subjects: X-ray techniques: radiography and computed tomography (biomedical imaging/measurement); Optical, image and video signal processing; X-rays and particle beams (medical uses); Patient diagnostic methods and instrumentation; Biology and medical computing; Integral transforms; Computer vision and image processing techniques; Medical magnetic resonance imaging and spectroscopy; Integral transforms; Biomedical magnetic resonance imaging and spectroscopy; Neural computing techniques; Function theory, analysis

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