Journal of Cardiothoracic Surgery | |
A new human heart vessel identification, segmentation and 3D reconstruction mechanism | |
Mohd Zamrin Dimon1  Fatimah Khalid2  Ramlan Mahmod2  Rahmita Wirza2  Aqeel Al-Surmi2  | |
[1] Cardiothoracic Unit, Surgical Cluster, Faculty of Medicine, Universiti Teknologi MARA, Selangor, Malaysia;Department of Multimedia, Faculty of Computer Science and Information Technology, University Putra Malaysia, Selangor, Malaysia | |
关键词: Vessel segmentations; Image enhancement; Heart surgery; 3D Model; | |
Others : 1151745 DOI : 10.1186/s13019-014-0161-1 |
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received in 2014-04-19, accepted in 2014-09-25, 发布年份 2014 | |
【 摘 要 】
Background
The identification and segmentation of inhomogeneous image regions is one of the most challenging issues nowadays. The surface vessels of the human heart are important for the surgeons to locate the region where to perform the surgery and to avoid surgical injuries. In addition, such identification, segmentation, and visualisation helps novice surgeons in the training phase of cardiac surgery.
Methods
This article introduces a new mechanism for identifying the position of vessels leading to the performance of surgery by enhancement of the input image. In addition, develop a 3D vessel reconstruction out of a single-view of a real human heart colour image obtained during open-heart surgery.
Results
Reduces the time required for locating the vessel region of interest (ROI). The vessel ROI must appear clearly for the surgeons. Furthermore, reduces the time required for training cardiac surgery of the novice surgeons. The 94.42% accuracy rate of the proposed vessel segmentation method using RGB colour space compares to other colour spaces.
Conclusions
The advantage of this mechanism is to help the surgeons to perform surgery in less time, avoid surgical errors, and to reduce surgical effort. Moreover, the proposed technique can reconstruct the 3D vessel model from a single image to facilitate learning of the heart anatomy as well as training of cardiac surgery for the novice surgeons. Furthermore, extensive experiments have been conducted which reveal the superior performance of the proposed mechanism compared to the state of the art methods.
【 授权许可】
2014 Al-Surmi et al.; licensee BioMed Central Ltd.
【 预 览 】
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【 参考文献 】
- [1][http:/ / www.maquet.com/ int/ product/ ACROBAT-i-Positioner-System?parentN odeId=hae1lgnr&tab=Overview] webcite ACROBAT-i Positioner System. []
- [2]Wyszecki G, Stiles WS: Color Science. Wiley, New York; 1982.
- [3]Travis D: Effective Color Displays: Theory and Practice. Academic, London; 1991.
- [4]Ford A, Roberts A: Colour Space Conversions. Westminster University, London, UK; 1998.
- [5]Pohle R, Toennies KD: Segmentation of medical images using adaptive region growing. Proc SPIE Medical Imaging 2001, 1337-1346.
- [6]Eiho S, Sekiguchi H, Sugimoto N, Hanakawa T, Urayama S: Branch-based region growing method for blood vessel segmentation. Proceedings of International Society for Photogrammetry and Remote Sensing Congress 2004, 796-801.
- [7]Kittler J, Illingworth J, Föglein J: Threshold selection based on a simple image statistic. Comput Vis Graph Image Proc 1985, 30:125-147.
- [8]Davies ER: Computer and Machine Vision, Fourth Edition: Theory, Algorithms, Practicalities. Academic Press, USA; 2012.
- [9]Galic S, Loncaric S: Spatio-temporal image segmentation using optical flow and clustering algorithm. Image and Signal Processing and Analysis, 2000 IWISPA 2000 Proceedings of the First International Workshop on; 2000 2000, 63-68.
- [10]Ilea DE, Ghita O, Robinson K, Sadleir R, Lynch M, Brennan D, Whelan PF: Identification of Body Fat Tissues in MRI Data. 2004.
- [11]Gonzalez RC, Woods RE: Digital Image Processing. Prentice Hall, USA; 2008.
- [12]Haris K, Efstratiadis SN, Maglaveras N, Katsaggelos AK: Hybrid image segmentation using watersheds and fast region merging. IEEE Trans Image Process 1998, 7:1684-1699.
- [13]Felkel P, Wegenkittl R, Kanitsar A: Vessel tracking in peripheral CTA datasets-an overview. In Computer Graphics, Spring Conference on; Budmerice. IEEE; 2001:232¿239.
- [14]Suri JS, Liu K, Reden L, Laxminarayan S: A review on MR vascular image processing algorithms: acquisition and prefiltering: part I. IEEE Trans Inform Technol Biomed 2002, 6:324.
- [15]Suri JS, Liu K, Reden L, Laxminarayan S: A review on MR vascular image processing: skeleton versus nonskeleton approaches: part II. IEEE Trans Inform Technol Biomed 2002, 6:338-350.
- [16]Bühler K, Felkel P, La Cruz A: Geometric Methods for Vessel Visualization and Quantification¿A Survey. Springer, Berlin Heidelberg; 2004.
- [17]Kirbas C, Quek F: A review of vessel extraction techniques and algorithms. ACM Comput Surv (CSUR) 2004, 36:81-121.
- [18]Lesage D, Angelini ED, Bloch I, Funka-Lea G: A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes. Med Image Anal 2009, 13:819-845.
- [19]Al-Surmi A, Wirza R, Dimon MZ, Mahmod R, Khalid F: Three Dimensional Reconstruction of Human Heart Surface from Single Image-View under Different Illumination Conditions. Am J Appl Sci 2013, 10(7):669-680.
- [20]Stretch D: Algorithm Theoretical Basis Document. 1996.
- [21]Buss SR: 3D Computer Graphics: A Mathematical Introduction with OpenGL. Cambridge University Press, UK; 2003.
- [22]Han J, Kamber M, Pei J: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, USA; 2011.
- [23]Higgins W, Spyra W, Ritman E: Automatic extraction of the arterial tree from 3-D angiograms. In Engineering in Medicine and Biology Society. Images of the Twenty-First Century, Proceedings of the Annual International Conference of the IEEE Engineering in; Seattle, WA. IEEE; 1989:563¿564.
- [24]Niki N, Kawata Y, Satoh H, Kumazaki T: 3D imaging of blood vessels using x-ray rotational angiographic system. In Nuclear Science Symposium and Medical Imaging Conference IEEE Conference Record; San Francisco, CA. IEEE; 1993:1873¿1877.
- [25]Guo D, Richardson P: Automatic vessel extraction from angiogram images. In Computers in Cardiology; Cleveland, OH. IEEE; 1998:441¿444.
- [26]Sato Y, Nakajima S, Shiraga N, Atsumi H, Yoshida S, Koller T, Gerig G, Kikinis R: Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Med Image Anal 1998, 2:143-168.
- [27]Sarwal A, Dhawan AP: 3-d reconstruction of coronary arteries. In Engineering in Medicine and Biology Society Engineering Advances: New Opportunities for Biomedical Engineers Proceedings of the 16th Annual International Conference of the IEEE; Baltimore, MD. ?: IEEE; 1994:504¿505.
- [28]Frangi AF, Niessen WJ, Viergever MA: Three-dimensional modeling for functional analysis of cardiac images, a review. IEEE Trans Med Imaging 2001, 20:2-5.
- [29]Petitjean C, Dacher J-N: A review of segmentation methods in short axis cardiac MR images. Med Image Anal 2011, 15:169-184.
- [30]Bankhead P, Scholfield CN, McGeown JG, Curtis TM: Fast retinal vessel detection and measurement using wavelets and edge location refinement. PLoS One 2012, 7:e32435.
- [31]Vermes E, Childs H, Carbone I, Barckow P, Friedrich MG: Auto-Threshold quantification of late gadolinium enhancement in patients with acute heart disease. J Magn Reson Imaging 2013, 37:382-390.