A model-based edge estimation method with increased edge localization accuracy for medical images


ÖZTÜRK M. , KAYIKÇIOĞLU T.

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, cilt.23, ss.1127-1141, 2015 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 23 Konu: 4
  • Basım Tarihi: 2015
  • Doi Numarası: 10.3906/elk-1212-166
  • Dergi Adı: TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
  • Sayfa Sayıları: ss.1127-1141

Özet

This study proposes an improved method based on a nonlinear parametric model for intensity profile to increase the edge localization accuracy in medical images gathered from different imaging modalities. The edge model consists of three parameters associated with an edge point. It also takes into account local background intensity, noise, and blurring. The Marquardt-Levenberg algorithm is used to estimate the parameters because of its accuracy and good convergence rate. Performance of the proposed method is tested quantitatively by comparing the results with those of the well-known active contour method on synthetic vessel images. Qualitative comparisons on real MRI, coronary angiography, CT, ultrasound, and retinal images showed that the proposed method accurately estimates edges in medical images.

This study proposes an improved method based on a nonlinear parametric model for intensity profile to increase the edge localization accuracy in medical images gathered from different imaging modalities. The edge model consists of three parameters associated with an edge point. It also takes into account local background intensity, noise, and blurring. The Marquardt–Levenberg algorithm is used to estimate the parameters because of its accuracy and good convergence rate. Performance of the proposed method is tested quantitatively by comparing the results with those of the well-known active contour method on synthetic vessel images. Qualitative comparisons on real MRI, coronary angiography, CT, ultrasound, and retinal images showed that the proposed method accurately estimates edges in medical images.