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Comput Methods Programs Biomed. 2016 Oct;134:237-58. doi: 10.1016/j.cmpb.2016.07.009. Epub 2016 Jul 9.

Five multiresolution-based calcium volume measurement techniques from coronary IVUS videos: A comparative approach.

Author information

1
Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India.
2
Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan.
3
Cardiovascular Medicine, National Center for Global Health and Medicine, Tokyo, Japan.
4
Dept. MAIA, Computer Vision Centre, Cerdanyola del Vallés, University of Barcelona, Spain.
5
Department of Bioengineering, University of Louisville, USA.
6
Department of Radiology, University of Cagliari, Italy.
7
Vascular Screening and Diagnostic Centre, London, UK; Vascular Diagnostic Centre, University of Cyprus, Nicosia, Cyprus.
8
CorVasc Vascular Laboratory, 8433 Harcourt Rd #100, Indianapolis, IN, USA.
9
UC Davis Vascular Centre, University of California, Davis, CA, USA.
10
Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA; Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA; Department of Electrical Engineering, University of Idaho (Affl.), ID, USA. Electronic address: jasjit.suri@atheropoint.com.

Abstract

BACKGROUND AND OBJECTIVE:

Fast intravascular ultrasound (IVUS) video processing is required for calcium volume computation during the planning phase of percutaneous coronary interventional (PCI) procedures. Nonlinear multiresolution techniques are generally applied to improve the processing time by down-sampling the video frames.

METHODS:

This paper presents four different segmentation methods for calcium volume measurement, namely Threshold-based, Fuzzy c-Means (FCM), K-means, and Hidden Markov Random Field (HMRF) embedded with five different kinds of multiresolution techniques (bilinear, bicubic, wavelet, Lanczos, and Gaussian pyramid). This leads to 20 different kinds of combinations. IVUS image data sets consisting of 38,760 IVUS frames taken from 19 patients were collected using 40 MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5 mm/sec.). The performance of these 20 systems is compared with and without multiresolution using the following metrics: (a) computational time; (b) calcium volume; (c) image quality degradation ratio; and (d) quality assessment ratio.

RESULTS:

Among the four segmentation methods embedded with five kinds of multiresolution techniques, FCM segmentation combined with wavelet-based multiresolution gave the best performance. FCM and wavelet experienced the highest percentage mean improvement in computational time of 77.15% and 74.07%, respectively. Wavelet interpolation experiences the highest mean precision-of-merit (PoM) of 94.06 ± 3.64% and 81.34 ± 16.29% as compared to other multiresolution techniques for volume level and frame level respectively. Wavelet multiresolution technique also experiences the highest Jaccard Index and Dice Similarity of 0.7 and 0.8, respectively. Multiresolution is a nonlinear operation which introduces bias and thus degrades the image. The proposed system also provides a bias correction approach to enrich the system, giving a better mean calcium volume similarity for all the multiresolution-based segmentation methods. After including the bias correction, bicubic interpolation gives the largest increase in mean calcium volume similarity of 4.13% compared to the rest of the multiresolution techniques. The system is automated and can be adapted in clinical settings.

CONCLUSIONS:

We demonstrated the time improvement in calcium volume computation without compromising the quality of IVUS image. Among the 20 different combinations of multiresolution with calcium volume segmentation methods, the FCM embedded with wavelet-based multiresolution gave the best performance.

KEYWORDS:

Calcium volume; Computational time; Coronary artery; IVUS; Multiresolution; Precision-of-merit

PMID:
27480747
DOI:
10.1016/j.cmpb.2016.07.009
[Indexed for MEDLINE]

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