Earticle

초록

영어

Image registration refers to finding a geometrical transformation that correspond any point from one image to its homologous on the other image. There are several similarity measures that are classified in two groups based on features and intensity. In medical imaging, accuracy of registration algorithm is important. Since intensity-based methods, are more accurate than feature based ones, we select intensity-based registration; But intensity based methods usually need to global or local similarity measure optimization. Due to large search space, global methods optimization is time-consuming and when image irregularities are large, local methods cannot reach to an optimum amount. Despite these challenges, we found that learning based methods can be an appropriate policy to overcome these problems.
Accordingly, in this paper, instead of using a fixed similarity measure, learning based similarity measure methods will present. Using the presented approaches in this paper can have been an effective role in analyzing and evaluating multi modal medical image registration and will increase three main functional measures – accuracy, speed and robustness – in medical image registration.

목차

Abstract
 1. Introduction
 2. Research Background
 3. Learning based Method vs Other Image Registration Similarity Measures
 4. Learning based Methods for Multimodal Medical Image Registration
  4.1. Max-margin Algorithm
  4.2. Kullback-liebler
  4.3. Jensen –Shannon Divergence
  4.4. Genetic Algorithm
  4.5. PSO
  4.6. Neural Network
 5. Evaluation
 6. Conclusion and Future Work
 References

키워드

저자

• Mohammad-Reza Keyvanpour [ Department of Computer Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran ]
• Somayeh Alehojat [ Department of Computer Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605