Efficiently compressing 3D medical images for teleinterventions via CNNs and anisotropic diffusion
Tóm tắt
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.
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 (
The results show that the method can significantly improve
We thus conclude that the method has a high potential to be applied in teleintervention applications.
Từ khóa
Tài liệu tham khảo
SrivastavaS PantM AbrahamA AgrawalN.The Technological Growth in eHealth Services 2015 ISSN: 1748‐670X Pages: e894171 Publisher: Hindawi Volume: 2015.
Hanoi Medical University Hospital ‐ HMU Hospital ‐ YouTube.https://www.youtube.com/watch?v=Nyu9D1y‐HnI. Accessed September 24 2020.
SingLIVE 2020: Scaling Greater Heights ‐ SingHealth.https://www.sgh.com.sg/news/education/singlive‐2020‐scaling‐greater‐heights;https://www.emedevents.com/c/medical‐conferences‐2020/singapore‐live‐2020;https://www.nhcs.com.sg/about‐us/newsroom/Documents/NHCS_Murmurs_Issue36‐WEB.pdf. Accessed September 24 2020.
KaufmanJA LeeMJ Vascular and Interventional Radiology. The Requisites E‐Book Elsevier Health Sciences.2013.https://www.elsevier.com/books/vascular‐and‐interventional‐radiology‐the‐requisites/kaufman/978‐0‐323‐04584‐1
AhadA TahirM YauK‐LA.5G‐Based Smart Healthcare Network: Architecture Taxonomy Challenges and Future Research Directions IEEE Access 7 Conference Name: IEEE Access;2019:100747–100762.
LammleT.CompTIA Network+ Study Guide: Exam N10–007 4th edn. Comptia Network + Study Guide Authorized Courseware Sybex;2018.
KumarS KrupinskiE Teleradiology Springer Science & Business Media 2008.
ClunieDA.Lossless compression of grayscale medical images: effectiveness of traditional and state‐of‐the‐art approaches in Medical Imaging 2000: PACS Design and Evaluation: Engineering and Clinical Issues volume 3980 International Society for Optics and Photonics.2000:74–84.
ParikhS KalvaH AdzicV.Evaluation of HEVC compression for high bit depth medical images in 2016 IEEE International Conference on Consumer Electronics (ICCE) 2016:311–314 ISSN: 2158‐4001.
UmaMaheswari S, 2020, Lossless medical image compression algorithm using tetrolet transformation, J Ambient Int Human Comput, 1
HulskenB.Fast Compression Method for Medical Images on the Web arXiv:2005.08713 [eess] (2020) arXiv: 2005.08713.https://arxiv.org/abs/2005.08713
AhmadiM EmamiA HajabdollahiM et al.Lossless Compression of Angiogram Foreground with Visual Quality Preservation of. Background. 2018;arXiv:1802.07769. arXiv:1802.07769 [cs].
MoorthiM AmuthaR.A near Lossless compression method for medical images in IEEE‐International Conference On Advances In Engineering. Science And Management (ICAESM ‐2012) 2012:39–44.
LuuHM.Image Analysis for Guidance in Minimally Invasive Liver Interventions Number: 369.2017.
ChristPF EttlingerF GrünF et al.Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional. Neural Networks.2017;arXiv:1702.05970. arXiv:1702.05970 [cs].
Son HH, 2020, Liver segmentation on a variety of computed tomography (CT) images based on convolutional neural networks combined with connected components, VNU J Sci, 36, 1
TariqZB ArshadN NabeelM.Enhanced LZMA and BZIP2 for improved energy data compression in 2015 International Conference on Smart Cities and Green ICT Systems (SMARTGREENS);2015:1–8.
PrangnellL Visually lossless coding in HEVC: A high bit depth and 4: 4: 4 capable JND‐based perceptual quantisation technique for HEVC Signal Processing: Image Communication2018;63:125–140.
RonnebergerO FischerP BroxT U‐Net: Convolutional Networks for Biomedical Image Segmentation 2015; arXiv:1505.04597. arXiv:1505.04597 [cs].
HoangHS Phuong PhamC FranklinD vanWalsumT Ha LuuM.An Evaluation of CNN‐based Liver Segmentation Methods using Multi‐types of CT Abdominal Images from Multiple Medical Centers in 2019 19th International Symposium on Communications and Information Technologies (ISCIT) 2019:20–25 ISSN: 2643‐6175.
Cover TM, 1999, Elements of Information Theory
BurrowsM WheelerDJ.A block‐sorting lossless data compression algorithm. Technical report.1994.
PatelRA ZhangY MakJ DavidsonA OwensJD Parallel lossless data compression on the GPU in 2012 Innovative Parallel Computing (InPar) 2012:1–9.
SanchezV Bartrina‐RapestaJ.Lossless compression of medical images based on HEVC intra coding in 2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP);2014:6622–6626 ISSN: 2379‐190X.
PoleA ShriramR.3‐D Medical Image Compression by Using HEVC. In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA);2018:1–5.
Luu HM, 2016, Non‐rigid registration of liver CT images for CT‐guided ablation of liver tumors, PLOS ONE 11, e0161600
AnderssonP NilssonJ Akenine‐MöllerT OskarssonM ÅströmK FairchildMD FLIP: A Difference Evaluator for Alternating Images Proceedings of the ACM on Computer Graphics and Interactive Techniques2020;3:1–23.
BilicP The Liver Tumor Segmentation Benchmark (LiTS) 2019; arXiv:1901.04056 [cs] arXiv: 1901.04056 version: 1.
BrossB ChenJ LiuS.Versatile video coding (Draft 5). JVET‐K10012018.
Kalaiselvi T, 2018, Performance of medical image processing algorithms implemented in CUDA running on GPU based machine, Int J Intell Syst App, 10, 58
WHO CORONAVIRUS DISEASE (COVID‐19) Dashboard.https://covid19.who.int/. Accessed October 19 2020.