Efficient 3D medical image segmentation algorithm over a secured multimedia network

Image segmentation has proved its importance and plays an important role in various domains such as health systems and satellite-oriented military applications. In this context, accuracy, image quality, and execution time deem to be the major issues to always consider. Although many techniques have been applied, and their experimental results have shown appealing achievements for 2D images in real-time environments, however, there is a lack of works about 3D image segmentation despite its importance in improving segmentation accuracy. Specifically, HMM was used in this domain. However, it suffers from the time complexity, which was updated using different accelerators. As it is important to have efficient 3D image segmentation, we propose in this paper a novel system for partitioning the 3D segmentation process across several distributed machines. The concepts behind distributed multimedia network segmentation were employed to accelerate the segmentation computational time of training Hidden Markov Model (HMMs). Furthermore, a secure transmission has been considered in this distributed environment and various bidirectional multimedia security algorithms have been applied. The contribution of this work lies in providing an efficient and secure algorithm for 3D image segmentation. Through a number of extensive experiments, it was proved that our proposed system is of comparable efficiency to the state of art methods in terms of segmentation accuracy, security and execution time.

[1]  Fazirulhisyam Hashim,et al.  Design and performance analysis of high reliability-optimal routing protocol for mobile wireless multimedia sensor networks , 2017, 2017 IEEE 13th Malaysia International Conference on Communications (MICC).

[2]  Aly A. Farag,et al.  A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data , 2002, IEEE Transactions on Medical Imaging.

[3]  Ke Lu,et al.  Multi-manifold Sparse Graph Embedding for Multi-modal Image Classification , 2016, Neurocomputing.

[4]  Shadi AlZu'bi,et al.  3D multiresolution statistical approaches for accelerated medical image and volume segmentation , 2011 .

[5]  Philippe Delachartre,et al.  Automatic Segmentation of the Cerebral Ventricle in Neonates Using Deep Learning with 3D Reconstructed Freehand Ultrasound Imaging , 2018, 2018 IEEE International Ultrasonics Symposium (IUS).

[6]  O. Figueiredo.  Advances in discrete geometry applied to the extraction of planes and surfaces from 3D volumes , 1999 .

[7]  Al Zu'bi,et al.  3D multiresolution statistical approaches for accelerated medical image and volume segmentation , 2011 .

[8]  Mhd Saeed Sharif,et al.  Multi-resolution analysis using curvelet and wavelet transforms for medical imaging , 2011, 2011 IEEE International Symposium on Medical Measurements and Applications.

[9]  Montgomery Dwg,et al.  Multiscale compression and segmentation of volumetric oncological PET imagery. , 2006 .

[10]  Nan Zhao,et al.  Multi-band blending of aerial images using GPU acceleration , 2017, 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI).

[11]  Ke Lu,et al.  Low-Rank Discriminant Embedding for Multiview Learning , 2017, IEEE Transactions on Cybernetics.

[12]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[13]  Yoong Choon Chang,et al.  Adaptive error protection for video transmission over ultra-wideband wireless multimedia sensor networks , 2011, The International Conference on Information Networking 2011 (ICOIN2011).

[14]  Ewa Pietka,et al.  Fuzzy Clustering in Segmentation of Abdominal Structures Based on CT Studies , 2008, Information Technologies in Biomedicine.

[15]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[16]  Shadi AlZu'bi,et al.  Multiresolution Analysis Using Wavelet, Ridgelet, and Curvelet Transforms for Medical Image Segmentation , 2011, Int. J. Biomed. Imaging.

[17]  Pawel Badura,et al.  Fuzzy Connectedness in Segmentation of Medical Images - A Look at the Pros and Cons , 2011, IJCCI.

[18]  Frank Y. Shih,et al.  Image Processing and Mathematical Morphology: Fundamentals and Applications , 2017 .

[19]  Manuel Desco,et al.  Assessment of intraoperative 3D imaging alternatives for IOERT dose estimation. , 2017, Zeitschrift fur medizinische Physik.

[20]  Wei Liu,et al.  Multimedia Hashing and Networking , 2016, IEEE MultiMedia.

[21]  Joon Beom Seo,et al.  Validation of a CT-guided intervention robot for biopsy and radiofrequency ablation: experimental study with an abdominal phantom. , 2017, Diagnostic and interventional radiology.

[22]  Demetri Terzopoulos,et al.  Deformable models in medical image analysis: a survey , 1996, Medical Image Anal..

[23]  William A. Barrett,et al.  Interactive Segmentation with Intelligent Scissors , 1998, Graph. Model. Image Process..

[24]  Cheolkon Jung,et al.  A fast deconvolution-based approach for single-image super-resolution with GPU acceleration , 2018, Journal of Real-Time Image Processing.

[25]  Ayman El-Baz,et al.  Retinal Layers OCT Scans 3-D Segmentation , 2019, 2019 IEEE International Conference on Imaging Systems and Techniques (IST).

[26]  Yaser Jararweh,et al.  Parallel implementation for 3D medical volume fuzzy segmentation , 2020, Pattern Recognit. Lett..

[27]  Stefan O Schoenberg,et al.  Accuracy of percutaneous soft-tissue interventions using a multi-axis, C-arm CT system and 3D laser guidance. , 2015, European journal of radiology.

[28]  Ewa Pietka,et al.  Watershed based intelligent scissors , 2015, Comput. Medical Imaging Graph..

[29]  Yaser Jararweh,et al.  An optimal multi-processor allocation algorithm for high performance GPU accelerators , 2011, 2011 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT).

[30]  Peter Kazanzides,et al.  Intraoperative Image-based Multiview 2D/3D Registration for Image-Guided Orthopaedic Surgery: Incorporation of Fiducial-Based C-Arm Tracking and GPU-Acceleration , 2012, IEEE Transactions on Medical Imaging.

[31]  Shadi AlZu'bi,et al.  Transferable HMM probability matrices in multi‐orientation geometric medical volumes segmentation , 2019, Concurr. Comput. Pract. Exp..

[32]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[33]  Kannan,et al.  ON IMAGE SEGMENTATION TECHNIQUES , 2022 .

[34]  Mahmoud Al-Ayyoub,et al.  Enhanced 3D segmentation techniques for reconstructed 3D medical volumes: Robust and Accurate Intelligent System , 2017, EUSPN/ICTH.

[35]  Daguang Xu,et al.  Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation , 2020, IEEE Transactions on Medical Imaging.

[36]  Mingyu Chen,et al.  A Reliable Distributed Convolutional Neural Network for Biology Image Segmentation , 2015, 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[37]  Ronald Fedkiw,et al.  Level set methods and dynamic implicit surfaces , 2002, Applied mathematical sciences.

[38]  Shadi AlZu'bi,et al.  3D Multiresolution Analysis for reduced features segmentation of medical volumes using PCA , 2010, 2010 IEEE Asia Pacific Conference on Circuits and Systems.

[39]  Maysam Abbod,et al.  Enhanced Hidden Markov Models for accelerating medical volumes segmentation , 2011, 2011 IEEE GCC Conference and Exhibition (GCC).

[40]  Milan Sonka,et al.  Deep LOGISMOS: Deep learning graph-based 3D segmentation of pancreatic tumors on CT scans , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[41]  Hanchuan Peng,et al.  A hierarchical distributed genetic algorithm for image segmentation , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[42]  L.-K. Shark,et al.  Medical Image Segmentation Using New Hybrid Level-Set Method , 2008, 2008 Fifth International Conference BioMedical Visualization: Information Visualization in Medical and Biomedical Informatics.

[43]  Jian Zhou,et al.  Fast and efficient fully 3D PET image reconstruction using sparse system matrix factorization with GPU acceleration , 2011, Physics in medicine and biology.

[44]  Supun Samarasekera,et al.  Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation , 1996, CVGIP Graph. Model. Image Process..

[45]  Jens von Berg,et al.  A hybrid method for registration of interventional CT and ultrasound images , 2004, CARS.

[46]  Paolo Gamba,et al.  Towards distributed region growing image segmentation based on MapReduce , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[47]  Piotr Zarychta,et al.  Computer Assisted Location of the Lower Limb Mechanical Axis , 2012, ITIB.

[48]  Tarek Kanan,et al.  Multi-orientation geometric medical volumes segmentation using 3D multiresolution analysis , 2018, Multimedia Tools and Applications.

[49]  W. Wieclawek,et al.  Live-Wire-Based 3D Segmentation Method , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[50]  Dongjian He,et al.  3D Segmentation of Pulmonary Nodules Based on Multi-View and Semi-Supervised , 2020, IEEE Access.