# Image Segmentation for Cardiovascular Biomedical Applications at Different Scales

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Segmentation Techniques

#### 2.1. Segmentation of Abdominal Parenchymal Organs

- Pre-process and smooth the input dataset.
- Compute the spatial-dependence matrix (specified in [18]) for all voxels of the smoothed dataset.
- Compute entropy (1) for each voxel using the spatial-dependence matrix.
- Obtain the binary mask by entropy values thresholding.
- Set the seed points for organs extraction.
- Implement active contours method and extract 3D model.

#### 2.2. Vessel Segmentation

- Segment the aorta.
- Remove pulmonary arteries.
- (Cerebral case only) Darken bone intensities.
- Apply Frangi Vesselness filter.
- Locate the ostia points or aortic arch branches and segment the vessels.
- Clean aorta border.

- Compute the radius and the center c of the largest bright disk D on transverse planes using CHT method.
- Construct the connected region mask ${M}_{init}$ containing voxel c with the minimal intensity inside of D as the lower threshold.
- Obtain ${M}_{A}$ as a result of the IDT method applied to the mask ${M}_{init}$ and the seed c.
- Smooth the mask ${M}_{A}$ with the parameter r.
- (Cerebral case only) Copy mask ${M}_{A}$ to mask ${M}_{smooth}$. Delete R-border from ${M}_{smooth}$, then add $(R+t)$-border.
- (Cerebral case only) Intersect the mask ${M}_{A}$ with the mask ${M}_{smooth}$.

#### 2.3. Segmentation of Lipid Droplets

- Align all 2D slices of the image dataset.
- Detect all lipid droplets using the threshold method (ref. [17]).
- Apply Gaussian smoothing in the vicinity of the lipid droplet.
- Segment inhomogeneous inclusions by thresholding with the lower parameter.
- Apply 3D remove-islands procedure.

## 3. Results

## 4. Discussion

## 5. Conclusions

## Supplementary Materials

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- Dazzo, F.; Niccum, B. Use of CMEIAS Image Analysis Software to Accurately Compute Attributes of Cell Size, Morphology, Spatial Aggregation and Color Segmentation that Signify in situ Ecophysiological Adaptations in Microbial Biofilm Communities. Computation
**2015**, 3, 72–98. [Google Scholar] [CrossRef] - Kislitsyn, A.; Savinkov, R.; Novkovic, M.; Onder, L.; Bocharov, G. Computational Approach to 3D Modeling of the Lymph Node Geometry. Computation
**2015**, 3, 222–234. [Google Scholar] [CrossRef] - Sazonov, I.; Yeo, S.Y.; Bevan, R.L.T.; Xie, X.; van Loon, R.; Nithiarasu, P. Modelling pipeline for subject-specific arterial blood flow—A review. Int. J. Numer. Methods Biomed. Eng.
**2011**, 27, 1868–1910. [Google Scholar] [CrossRef] - Quarteroni, A.; Tuveri, M.; Veneziani, A. Computational vascular fluid dynamics: Problems, models and methods. Comput. Vis. Sci.
**2000**, 2, 163–197. [Google Scholar] [CrossRef] - Gerbeau, J.F.; Vidrascu, M.; Frey, P. Fluid–structure interaction in blood flows on geometries based on medical imaging. Comput. Struct.
**2005**, 83, 155–165. [Google Scholar] [CrossRef] - Holtzman-Gazit, M.; Kimmel, R.; Peled, N.; Goldsher, D. Segmentation of thin structures in volumetric medical images. IEEE Trans. Image Proc.
**2006**, 15, 354–363. [Google Scholar] [CrossRef] - Radaelli, A.G.; Peiró, J. On the segmentation of vascular geometries from medical images. Int. J. Numer. Methods Biomed. Eng.
**2010**, 26, 3–34. [Google Scholar] [CrossRef] - Yeo, S.Y.; Xie, X.; Sazonov, I.; Nithiarasu, P. Segmentation of biomedical images using active contour model with robust image feature and shape prior. Int. J. Numer. Methods Biomed. Eng.
**2014**, 30, 232–248. [Google Scholar] [CrossRef] [PubMed] - Rohlfing, T.; Brandt, R.; Menzel, R.; Russakoff, D.B.; Maurer, C.R. Quo Vadis, Atlas-Based Segmentation? In Handbook of Biomedical Image Analysis: Volume III: Registration Models; Springer: Boston, MA, USA, 2005; pp. 435–486. [Google Scholar]
- Isgum, I.; Staring, M.; Rutten, A.; Prokop, M.; Viergever, M.; van Ginneken, B. Multi-Atlas-Based Segmentation with Local Decision Fusion— Application to Cardiac and Aortic Segmentation in CT Scans. IEEE Trans. Med. Imaging
**2009**, 28, 1000–1010. [Google Scholar] [CrossRef] [PubMed] - Wang, H.; Suh, J.W.; Das, S.R.; Pluta, J.B.; Craige, C.; Yushkevich, P.A. Multi-Atlas Segmentation with Joint Label Fusion. IEEE Trans. Pattern Anal. Mach. Intell.
**2013**, 35, 611–623. [Google Scholar] [CrossRef] [PubMed] - Lines, G.; Buist, M.; Grottum, P.; Pullan, A.; Sundnes, J.; Tveito, A. Mathematical models and numerical methods for the forward problem in cardiac electrophysiology. Comput. Vis Sci.
**2002**, 5, 215–239. [Google Scholar] [CrossRef] - Zemzemi, N.; Bernabeu, M.O.; Saiz, J.; Cooper, J.; Pathmanathan, P.; Mirams, G.R.; Pitt-Francis, J.; Rodriguez, B. Computational assessment of drug-induced effects on the electrocardiogram: From ion channel to body surface potentials. Br. J. Pharmacol.
**2013**, 168, 718–733. [Google Scholar] [CrossRef] [PubMed] - Hughes, T.J.; Lubliner, J. On the one-dimensional theory of blood flow in the larger vessels. Math. Biosci.
**1973**, 18, 161–170. [Google Scholar] [CrossRef] - Zarins, C.K.; Taylor, C.A.; Min, J.K. Computed Fractional Flow Reserve (FFTCT) Derived from Coronary CT Angiography. J. Cardiovasc. Transl. Res.
**2013**, 6, 708–714. [Google Scholar] [CrossRef] [PubMed] - Morris, P.D.; Ryan, D.; Morton, A.C.; Lycett, R.; Lawford, P.V.; Hose, D.R.; Gunn, J.P. Virtual Fractional Flow Reserve from Coronary Angiography: Modeling the Significance of Coronary Lesions. JACC Cardiovasc. Interv.
**2013**, 6, 149–157. [Google Scholar] [CrossRef] [PubMed] - Sulkin, M.S.; Yang, F.; Holzem, K.M.; Leer, B.V.; Bugge, C.; Laughner, J.I.; Green, K.; Efimov, I.R. Nanoscale three-dimensional imaging of the human myocyte. J. Struct. Biol.
**2014**, 188, 55–60. [Google Scholar] [CrossRef] [PubMed] - Haralick, R.M.; Shanmugam, K.; Dinstein, I. Textural Features for Image Classification. IEEE Trans. Syst. Man Cybern.
**1973**, 3, 610–621. [Google Scholar] [CrossRef] - Frangi, A.; Niessen, W.; Vincken, K.; Viergever, M. Multiscale Vessel Enhancement Filtering. In Medical Image Computing and Computer-Assisted Interventation – MICCAI’98; Springer: Berlin/Heidelberg, Germany, 1998; pp. 130–137. [Google Scholar]
- Sethian, J. Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science; Number 3 in Cambridge Monographs on Applied and Computational Mathematics; Cambridge University Press: Cambridge, UK, 1999. [Google Scholar]
- Grady, L. Fast, Quality, Segmentation of Large Volumes—Isoperimetric Distance Trees. In Computer Vision—ECCV 2006; Springer: Berlin/Heidelberg, Germany, 2006; pp. 449–462. [Google Scholar]
- Pudney, C. Distance-Ordered Homotopic Thinning: A Skeletonization Algorithm for 3D Digital Images. Comput. Vis. Image Underst.
**1998**, 72, 404–413. [Google Scholar] [CrossRef] - Danilov, A.; Ivanov, Y.; Pryamonosov, R.; Vassilevski, Y. Methods of Graph Network Reconstruction in Personalized Medicine. Int. J. Numer. Methods Biomed. Eng.
**2016**, 32, e02754. [Google Scholar] [CrossRef] [PubMed] - Danilov, A.A.; Nikolaev, D.V.; Rudnev, S.G.; Salamatova, V.Y.; Vassilevski, Y.V. Modelling of bioimpedance measurements: Unstructured mesh application to real human anatomy. Russ. J. Numer. Anal. Math. Model.
**2012**, 27, 431–440. [Google Scholar] [CrossRef] - Vassilevski, Y.V.; Danilov, A.A.; Simakov, S.S.; Gamilov, T.M.; Ivanov, Y.A.; Pryamonosov, R.A.; Simakov, S.S. Patient-specific anatomical models in human physiology. Russ. J. Numer. Anal. Math. Model.
**2015**, 30, 185–201. [Google Scholar] [CrossRef] - Danilov, A.A.; Pryamonosov, R.A.; Yurova, A.S. Image segmentation techniques for biomedical modeling: Electrophysiology and hemodynamics. In Proceedings of the 7th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2016), Crete Island, Greece, 5–10 June 2016.
- Yang, G.; Kitslaar, P.; Frenay, M.; Broersen, A.; Boogers, M.J.; Bax, J.J.; Reiber, J.H.C.; Dijkstra, J. Automatic Centerline Extraction of Coronary Arteries in Coronary Computed Tomographic Angiography. Int. J. Cardiovasc. Imaging
**2012**, 28, 921–933. [Google Scholar] [CrossRef] [PubMed] - Tek, H. Automatic Coronary Tree Modeling. The Midas Journal—2008 MICCAI Workshop Grand Challenge Coronary Artery Tracking. 2008. Available online: http://hdl.handle.net/10380/1426 (accessed on 7 September 2016).
- Manniesing, R.; Viergever, M.A.; van der Lugt, A.; Niessen, W.J. Cerebral Arteries: Fully Automated Segmentation from CT Angiography—A Feasibility Study 1. Radiology
**2008**, 247, 841–846. [Google Scholar] [CrossRef] [PubMed] - Gao, X.; Uchiyama, Y.; Zhou, X.; Hara, T.; Asano, T.; Fujita, H. A Fast and Fully Automatic Method for Cerebrovascular Segmentation on Time-of-Flight (TOF) MRA Image. J. Digit. Imaging
**2011**, 24, 609–625. [Google Scholar] [CrossRef] [PubMed] - Ho, H.; Bier, P.; Sands, G.; Hunter, P. Cerebral artery segmentation with level set methods. In Proceedings of Image and Vision Computing New Zealand 2007, Hamilton, New Zealand, 5–7 December 2007; pp. 300–304.
- Cuisenaire, O. Fully automated segmentation of carotid and vertebral arteries from CTA. The Midas Journal—Carotid Lumen Segmentation and Stenosis Grading (Grand Challenge). 2009. Available online: http://hdl.handle.net/10380/3100 (accessed on 7 September 2016).
- Lucchi, A.; Smith, K.; Achanta, R.; Knott, G.; Fua, P. Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks with Learned Shape Features. IEEE Trans. Med. Imaging
**2012**, 31, 474–486. [Google Scholar] [CrossRef] [PubMed] - Keller, D.U.J.; Weber, F.M.; Seemann, G.; Dössel, O. Ranking the Influence of Tissue Conductivities on Forward-Calculated ECGs. IEEE Trans. Biomed. Eng.
**2010**, 57, 1568–1576. [Google Scholar] [CrossRef] [PubMed] - Zhao, B.; Colville, J.; Kalaigian, J.; Curran, S.; Jiang, L.; Kijewski, P.; Schwartz, L.H. Automated Quantification of Body Fat Distribution on Volumetric Computed Tomography. J. Comput. Assist. Tomogr.
**2006**, 30, 777–783. [Google Scholar] [CrossRef] [PubMed] - Armato, S.G.; Sensakovic, W.F. Automated lung segmentation for thoracic CT. Acad. Radiol.
**2004**, 11, 1011–1021. [Google Scholar] [CrossRef] [PubMed] - Zhang, J.; Yan, C.H.; Chui, C.K.; Ong, S.H. Fast segmentation of bone in CT images using 3D adaptive thresholding. Comput. Biol. Med.
**2010**, 40, 231–236. [Google Scholar] [CrossRef] [PubMed] - Chung, H.; Cobzas, D.; Birdsell, L.; Lieffers, J.; Baracos, V. Automated segmentation of muscle and adipose tissue on CT images for human body composition analysis. In Proceedings of the Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling, Lake Buena Vista, FL, USA, 7 February 2009.
- Danilov, A.; Kramarenko, V.; Nikolaev, D.; Yurova, A. Personalized model adaptation for bioimpedance measurements optimization. Russ. J. Numer. Anal. Math. Model.
**2013**, 28, 459–470. [Google Scholar] [CrossRef] - Achanta, R.; Shaji, A.; Smith, K.; Lucchi, A.; Fua, P.; Süsstrunk, S. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods. IEEE Trans. Pattern Anal. Mach. Intell.
**2012**, 34, 2274–2282. [Google Scholar] [CrossRef] [PubMed] - Campadelli, P.; Casiraghi, E.; Pratissoli, S.; Lombardi, G. Automatic Abdominal Organ Segmentation from CT images. Electron. Lett. Comput. Vis. Image Anal.
**2009**, 8, 1–14. [Google Scholar] - Chan, T.F.; Vese, L.A. Active Contours without Edges. IEEE Trans. Image Proc.
**2001**, 10, 266–277. [Google Scholar] [CrossRef] [PubMed] - Marquez-Neila, P.; Baumela, L.; Alvarez, L. A Morphological Approach to Curvature-Based Evolution of Curves and Surfaces. IEEE Trans. Pattern Anal. Mach. Intell.
**2014**, 36, 2–17. [Google Scholar] [CrossRef] [PubMed] - Yushkevich, P.A.; Piven, J.; Cody Hazlett, H.; Gimpel Smith, R.; Ho, S.; Gee, J.C.; Gerig, G. User-Guided 3D Active Contour Segmentation of Anatomical Structures: Significantly Improved Efficiency and Reliability. Neuroimage
**2006**, 31, 1116–1128. [Google Scholar] [CrossRef] [PubMed] - Van Andel, H.A.F.G.; Venema, H.W.; Streekstra, G.J.; van Straten, M.; Majoie, C.B.L.M.; den Heeten, G.J.; Grimbergen, C.A. Removal of Bone in CT Angiography by Multiscale Matched Mask Bone Elimination. Med. Phys.
**2007**, 34, 449–462. [Google Scholar] - Duda, R.O.; Hart, P.E. Use of the Hough Transformation to Detect Lines and Curves in Pictures. Commun. ACM
**1972**, 15, 11–15. [Google Scholar] [CrossRef] - Johnson, H.J.; McCormick, M.M.; Ibanez, L. The ITK Software Guide Book 2: Design and Functionality; Kitware, Inc.: Clifton Park, NY, USA, 2015; Volume 2. [Google Scholar]
- OsiriX. DICOM Image Sample Sets. Available online: http://www.osirix-viewer.com/datasets (accessed on 9 September 2016).
- Gamilov, T.; Pryamonosov, R.; Simakov, S. Modeling of patient-specific cases of atherosclerosis in carotid arteries. In Proceedings of the 7th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2016), Crete Island, Greece, 5–10 June 2016.
- Gamilov, T.; Kopylov, P.; Pryamonosov, R.; Simakov, S. Virtual Fractional Flow Reserve Assesment in Patient-Specific Coronary Networks by the 1D Model of Haemodynamics. Russ. J. Numer. Anal. Math. Model.
**2015**, 30, 269–276. [Google Scholar] [CrossRef] - Lucchi, A.; Smith, K.; Achanta, R.; Lepetit, V.; Fua, P. A Fully Automated Approach to Segmentation of Irregularly Shaped Cellular Structures in EM Images. In Medical Image Computing and Computer-Assisted Intervention—MICCAI 2010; Springer: Berlin/Heidelberg, Germany, 2010; pp. 463–471. [Google Scholar]

**Figure 1.**Entropy computed for two different neighborhood sizes: (

**a**) $3\times 3\times 3$ voxels and (

**b**) $5\times 5\times 5$ voxels.

**Figure 2.**Binary masks obtained from the results of entropy computation: (

**a**) $3\times 3\times 3$ voxels and (

**b**) $5\times 5\times 5$ voxels.

**Figure 3.**Segmentation of the lipid droplet. (

**a**) sharp image slice and (

**b**) its noisy thresholding result; (

**c**) blurred slice; and (

**d**) its thresholding result.

**Figure 4.**Intensity histogram (black line) of the cropped FIB dataset containing inhomogeneous lipid droplet; The red line shows the threshold for inhomogeneous inclusions; the blue line—the threshold of lipid droplet.

**Figure 7.**Segmentation errors near aorta border: (

**a**) coronary arteries may be either connected or disconnected with segmentation “leaks”; (

**b**) errors near ostia points show that false vesselness filter response may be both inside and outside of the aorta; (

**c**) errors in the cerebral case and (

**d**) result of our algorithm application. Green—aorta, red—mask of coronary vessels, purple—mask of cerebral vessels.

**Figure 8.**Results of cerebral arteries segmentation: (

**a**,

**b**) resulting segmentations, green—aorta, purple—cerebral arteries.

**Figure 9.**3D volume reconstruction of a lipid droplet. (

**a**) internal structure of lipid droplet; (

**b**) whole structure.

**Table 1.**Results of entropy feature computation for two Computed Tomography (CT) datasets: image dimensions and computation times for both Central Processing Unit (CPU) and Graphics Processing Unit (GPU) version of the program code.

Dataset Size, Voxels | CPU Time, s | GPU Time, s |
---|---|---|

$512\times 335\times 200$ | 735 | 139 |

$430\times 346\times 277$ | 1189 | 198 |

Dataset | Dataset 1 | Dataset 2 |
---|---|---|

Resolution | $512\times 512\times 501$ | $512\times 512\times 451$ |

Spacing | $0.76\times 0.76\times 0.80$ mm | $0.62\times 0.62\times 0.80$ mm |

Pulmonary removal | $7.76$ s | $7.04$ s |

Aorta segmentation | $16.61$ s | $15.33$ s |

Frangi Vesselness | $196.40$ s | $184.91$ s |

Aortic arch branches | $7.61$ s | $6.67$ s |

Aorta border cleaning | $7.39$ s | $6.76$ s |

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Danilov, A.; Pryamonosov, R.; Yurova, A.
Image Segmentation for Cardiovascular Biomedical Applications at Different Scales. *Computation* **2016**, *4*, 35.
https://doi.org/10.3390/computation4030035

**AMA Style**

Danilov A, Pryamonosov R, Yurova A.
Image Segmentation for Cardiovascular Biomedical Applications at Different Scales. *Computation*. 2016; 4(3):35.
https://doi.org/10.3390/computation4030035

**Chicago/Turabian Style**

Danilov, Alexander, Roman Pryamonosov, and Alexandra Yurova.
2016. "Image Segmentation for Cardiovascular Biomedical Applications at Different Scales" *Computation* 4, no. 3: 35.
https://doi.org/10.3390/computation4030035