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Inter-Greedy Technique for Fusion of Different Segmentation Strategies Leading to High-Performance Carotid IMT Measurement in Ultrasound Images

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Abstract

User-based estimation of intima-media thickness (IMT) of carotid arteries leads to subjectivity in its decision support systems, while being used as a cardiovascular risk marker. During automated computer-based decision support, we had developed segmentation strategies that follow three main courses of our contributions: (a) signal processing approach combined with snakes and fuzzy K-means (CULEXsa), (b) integrated approach based on seed and line detection followed by probability based connectivity and classification (CALEXsa), and (c) morphological approach with watershed transform and fitting (WS). These grayscale segmentation algorithms yielding carotid wall boundaries has certain bias along with their own merits. We recently developed a fusion technique that was helpful in removing bias which combines two carotid wall boundaries using ground truth as an ideal marker. Here we have extended this fusion concept by taking merits of these multiple boundaries, so called, Inter-Greedy (IG) approach. Further we estimate IMT from these fused boundaries from multiple sources. Starting from the technique with the overall least system error (the snake-based one), we iteratively swapped the vertices of the profiles until we minimized its overall distance with respect to ground truth. The fusion boundary was the Inter-Greedy boundary. We used the polyline distance metric for performance evaluation and error minimization. We ran the segmentation protocol over the database of 200 carotid longitudinal B-mode ultrasound images and compared the performance of all the four techniques (CALEXia, CULEXsa, WS, IG). The mean error of Inter-Greedy technique yielded 0.32 ± 0.44 pixel (20.0 ± 27.5 µm) for the LI boundary (a 33.3% ± 5.6% improvement over initial best performing technique) and 0.21 ± 0.34 pixel (13.1 ± 21.3 µm) for MA boundary (a 32.3% ± 6.7% improvement). IMT measurement error for Greedy method was 0.74 ± 0.75 pixel (46.3 ± 46.9 µm), a 43.5% ± 2.4% improvement.

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Acknowledgments

The Authors would like to express their gratitude to Dr. William Liboni (Neurology Division of the Gradenigo Hospital, Torino, Italy) for having provided the data and helped in manual segmentation. The Authors are also grateful to Dr. Pierangela Giustetto (Visiting Scientist, Gradenigo Hospital, Torino, Italy) for her help in manual segmentation.

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Authors and Affiliations

  1. Biolab—Dipartimento di Elettronica, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129, Torino, Italy

    Filippo Molinari

  2. Mayo Clinic, Rochester, MN, USA

    Guang Zeng

  3. Biomedical Technologies Inc., Denver, CO, USA & Idaho State University (Aff.), Denver, ID, USA

    Jasjit S. Suri

Authors
  1. Filippo Molinari
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  2. Guang Zeng
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  3. Jasjit S. Suri
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Correspondence to Filippo Molinari.

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Molinari, F., Zeng, G. & Suri, J.S. Inter-Greedy Technique for Fusion of Different Segmentation Strategies Leading to High-Performance Carotid IMT Measurement in Ultrasound Images. J Med Syst 35, 905–919 (2011). https://doi.org/10.1007/s10916-010-9507-y

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  • DOI: https://doi.org/10.1007/s10916-010-9507-y

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