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Tytuł artykułu

Automated Characterization of Atheromatous Plaque in Intravascular Ultrasound Images Using Neuro Fuzzy Classifier

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Języki publikacji
EN
Abstrakty
EN
The medical imaging field has grown significantly in recent years and demands high accuracy since it deals with human life. The idea is to reduce human error as much as possible by assisting physicians and radiologists with some automatic techniques. The use of artificial intelligent techniques has shown great potential in this field. Hence, in this paper the neuro fuzzy classifier is applied for the automated characterization of atheromatous plaque to identify the fibrotic, lipidic and calcified tissues in Intravascular Ultrasound images (IVUS) which is designed using sixteen inputs, corresponds to sixteen pixels of instantaneous scanning matrix, one output that tells whether the pixel under consideration is Fibrotic, Lipidic, Calcified or Normal pixel. The classification performance was evaluated in terms of sensitivity, specificity and accuracy and the results confirmed that the proposed system has potential in detecting the respective plaque with the average accuracy of 98.9%.
Twórcy
autor
  • Department of Electronics and Communication Engineering, MEPCO Schlenk Engineering College, Sivakasi, Virudhunagar 626 005, Tamilnadu, India
autor
  • Department of Electronics and Communication Engineering, MEPCO Schlenk Engineering College, Sivakasi, Virudhunagar 626 005, Tamilnadu, India
autor
  • Department of Electronics and Communication Engineering, MEPCO Schlenk Engineering College, Sivakasi, Virudhunagar 626 005, Tamilnadu, India
Bibliografia
  • [1] World Health Organization, „Fact sheet No. 317”, September 2011, [Online] Available: http://www.who.int/cardiovasculardiseases/en.
  • [2] A. Katouzian, S. Sathyanarayana, B. Baseri, E. E. Konofagou, and S. G. Carlier, „Challenges in Atherosclerotic Plaque Characterization with Intravascular Ultrasound (IVUS): From Data Collection to Classification”, IEEE Transaction on Information Technology in Biomedicine, vol. 12, no. 3, pp. 315-327, 2008.
  • [3] E. C. Kyriacou, C. Pattichis, M. Pattichis, C. Loizou, C. Christodoulou, S. K. Kakkos, and A. Nicolaides, „A Review of Noninvasive Ultrasound Image Processing Methods in the Analysis of Carotid Plaque Morphology for the Assessment of Stroke Risk”, IEEE Transaction on Information Technology in Biomedicine, vol. 14, no. 4, pp. 1027-1037, 2010.
  • [4] L. S. Athanasiou, P. S. Karvelis, V. D. Tsakanikas, K. A. Stefanou, K. K. Naka, L. K. Michalis, G. A. Rigas, and D. I. Fotiadis, „Atherosclerotic plaque characterization using geometrical features from virtual histology intravascular ultrasound images”, in 10th IEEE International Conference on Information Technology and Applications in Biomedicine, 2010, pp. 1-4.
  • [5] N. N. Tsiaparas, S. Golemati, I. Andreadis, J. S. Stoitsis, I. Valavanis, and K. S. Nikita, „Comparison of Multiresolution Features for Texture Classification of Carotid Atherosclerosis From B-Mode Ultrasound”, IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 1, pp. 130-137, 2011.
  • [6] C. P. Loizou, V. Murray, M. S. Pattichis, and C. S. Pattichis, „Multiscale Amplitude-Modulation Frequency-Modulation (AMFM) Texture Analysis of Ultrasound Images of the Intima and Media Layers of the Carotid Artery”, IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 2, pp. 178-187, 2011.
  • [7] J. C. Seabra, F. Ciompi, O. Pujol, J. Mauri, P. Radeva, and J. Sanches, „Rayleigh Mixture Model for Plaque Characterization in Intravascular Ultrasound”, IEEE Transactions on Biomedical Engineering, vol. 58, no. 5, pp. 1314-1323, 2011.
  • [8] T. Koga, E. Uchino, and N. Suetake, „Automated boundary extraction and visualization system for coronary plaque in IVUS image by using fuzzy inference-based method”, IEEE International Conference on Fuzzy Systems, pp. 1966-1973, 2011.
  • [9] M. S. Hosseini and M. Zekri, „A review of medical image classification using Adaptive Neuro-Fuzzy Inference System(ANFIS)”, Journal of Medical Signals and Sensors, vol. 2, no. 1, pp. 51-62, 2012.
  • [10] R. Kaur, L. Kaur, and S. Gupta, „Enhanced K-Mean Clustering Algorithm for Liver Image Segmentation to Extract Cyst Region”, IJCA Special Issue on Novel Aspects of Digital Imaging Applications, pp. 59-66, 2011, DOI: 10.5120/4159-323.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWAD-0032-0018
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