A Selection and Reduction Approach for the Optimization of Ultrasound Carotid Artery Images Segmentation

  • Samanta Rosati
  • Gabriella Balestra
  • Filippo Molinari
  • U. Rajendra Acharya
  • Jasjit S. Suri
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 56)

Abstract

The segmentation of the carotid artery wall is an important aid to sonographers when measuring intima-media thickness (IMT). Automated and completely user-independent segmentation techniques are gaining increasing importance, because they avoid the bias coming from human interactions. However, automated techniques still underperform semi-automated IMT measurement methods. Automated techniques cannot reproduce human expertise in selecting the optimal point where IMT should be measured. Hence, superior intelligence must be embedded into automated techniques in order to overcome the performance limitations. A possible solution is to extract more information from the image, which could be obtained by an accurate analysis of the image at pixel level. In this study, we applied a feature selection and reduction approach to ultrasound carotid images, and measured 141 features for each image pixel and supposed that a pixel could belong to one of three classes: artery lumen, intima or media layer, or the adventitia layer. Among several approaches that are available for dimensional reduction, we chose to test three based on the Rough-Set Theory (RST): the QuickReduct Algorithm (QRA), the Entropy-Based Algorithm (EBR) and the Improved QuickReduct Algorithm (IQRA). QRA achieved the best performance and correctly classified 97.5 % of the pixels on a reduced testing image dataset and about 91.5 % for a large validation dataset. On average, QRA reduced the complexity of the system from 141 to 8 or 9 features. This result could represent a pilot study for developing an intelligent pre-classifier to improve the image segmentation performance of automated techniques in carotid ultrasound imaging.

Keywords

Ultrasound imaging Intima-media thickness Atherosclerosis Segmentation Feature extraction Feature selection Quickreduct algorithm Entropy Rough set Artificial neural networks 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Samanta Rosati
    • 1
  • Gabriella Balestra
    • 1
  • Filippo Molinari
    • 1
  • U. Rajendra Acharya
    • 2
    • 3
  • Jasjit S. Suri
    • 4
    • 5
  1. 1.Biolab, Department of Electronics and TelecommunicationsPolitecnico di TorinoTorinoItaly
  2. 2.Department of ECENgee Ann PolytechnicSingaporeSingapore
  3. 3.Faculty of Engineering, Department of Biomedical EngineeringUniversity of MalayaKuala LumpurMalaysia
  4. 4.Global Biomedical Technologies, IncRosevilleUSA
  5. 5.Biomedical Engineering DepartmentIdaho State UniversityPocatelloUSA

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