Lossless Compression of Angiogram Foreground with Visual Quality Preservation of Background

Ahmadi, Mahdi; Emami, Ali; Hajabdollahi, Mohsen; Soroushmehr, S. M. Reza; Karimi, Nader; Samavi, Shadrokh; Najarian, Kayvan

doi:10.1109/embc.2018.8513426

Cited by 2 publications

(1 citation statement)

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“…Recently, the machine learning approach has demonstrated superior performance over classical methods if a sufficient amount of training data is available 30 . Ahmadi et al (2018) 31 used a CNN for segmenting ROIs and background from an angiogram image. A DCT was used for tilling the ROIs and non‐ROIs, after which the image was smoothed via Gaussian blurring, followed by JPEG‐LS‐based compression.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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 (normalPSNR), structural similarity (italicSSIM), and compression ratio (italicCR) 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 italicCR of most well‐known compression methods. DLAD combined with HEVC‐visually lossless achieves the highest average italicCR 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 italicPSNR and italicSSIM 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.

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Section: Introductionmentioning

confidence: 99%