VNU-UET Repository

Efficiently compressing 3D medical images for teleinterventions via CNNs and anisotropic diffusion

Luu, Manh Ha and Xiem, HoangVan (2021) Efficiently compressing 3D medical images for teleinterventions via CNNs and anisotropic diffusion. Med Phys . 2021, 48 (6). pp. 2877-2890. ISSN 2473-4209

Full text not available from this repository.


Purpose: Efficient compression of images while preserving image quality has the potential to be a major enabler of effective remote clinical diagnosis and treatment, since poor Internet connection conditions are often the primary constraint in such services. This paper presents a framework for organ-specific image compression for teleinterventions based on a deep learning approach and anisotropic diffusion filter. Methods: The proposed method, deep learning and anisotropic diffusion (DLAD), uses a convolutional neural network architecture to extract a probability map for the organ of interest; this probability map guides an anisotropic diffusion filter that smooths the image except at the location of the organ of interest. Subsequently, a compression method, such as BZ2 and HEVC-visually lossless, is applied to compress the image. We demonstrate the proposed method on three-dimensional (3D) CT images acquired for radio frequency ablation (RFA) of liver lesions. We quantitatively evaluate the proposed method on 151 CT images using peak-signal-to-noise ratio (PSNR ), structural similarity ( SSIM ), and compression ratio ( CRCR ) metrics. Finally, we compare the assessments of two radiologists on the liver lesion detection and the liver lesion center annotation using 33 sets of the original images and the compressed images. Results: The results show that the method can significantly improve CRCR of most well-known compression methods. DLAD combined with HEVC-visually lossless achieves the highest average CRCR of 6.45, which is 36% higher than that of the original HEVC and outperforms other state-of-the-art lossless medical image compression methods. The means of PSNRPSNR and SSIMSSIM are 70 dB and 0.95, respectively. In addition, the compression effects do not statistically significantly affect the assessments of the radiologists on the liver lesion detection and the lesion center annotation. Conclusions: We thus conclude that the method has a high potential to be applied in teleintervention applications.

Item Type: Article
Subjects: Electronics and Communications > Electronics and Computer Engineering
Divisions: Faculty of Electronics and Telecommunications (FET)
Depositing User: Dr. Xiem HoangVan
Date Deposited: 13 Dec 2021 03:55
Last Modified: 13 Dec 2021 03:55

Actions (login required)

View Item View Item