Self-decisive algorithm for unconstrained optimization problems as in biomedical image analysis

This study describes the construction of a new algorithm where image processing along with the two-step quasi-Newton methods is used in biomedical image analysis. It is a well-known fact that medical informatics is an essential component in the perspective of health care. Image processing and imaging technology are the recent advances in medical informatics, which include image content representation, image interpretation, and image acquisition, and focus on image information in the medical field. For this purpose, an algorithm was developed based on the image processing method that uses principle component analysis to find the image value of a particular test function and then direct the function toward its best method for evaluation. To validate the proposed algorithm, two functions, namely, the modified trigonometric and rosenbrock functions, are tested on variable space.

[1]  C. Gu,et al.  A novel outlier detection method for monitoring data in dam engineering , 2022, Expert Syst. Appl..

[2]  Nudrat Aamir,et al.  Two-Step Skipping Techniques For Solution of Nonlinear Unconstrained Optimization Problems , 2021 .

[3]  Basim A. Hassan,et al.  A new class of self-scaling for quasi-newton method based on the quadratic model , 2021 .

[4]  Armin Rund,et al.  A hybrid semismooth quasi-Newton method for nonsmooth optimal control with PDEs , 2020, Optimization and Engineering.

[5]  Jiale Wang.  Application of improved Quasi-Newton method to the massive image denoising , 2018, Multimedia Tools and Applications.

[6]  K. Saravanakumar,et al.  Breast Cancer Detection using Image Processing Techniques , 2017 .

[7]  Marco Nolden,et al.  The Medical Imaging Interaction Toolkit , 2005, Medical Image Anal..

[8]  Baba C. Vemuri,et al.  Medical Image Analysis , 1999, IEEE Pulse.

[9]  Tamara G. Kolda,et al.  BFGS with Update Skipping and Varying Memory , 1998, SIAM J. Optim..

[10]  Issam A. R. Moghrabi,et al.  Multi-step quasi-Newton methods for optimization , 1994 .

[11]  Jorge J. Moré,et al.  Algorithm 566: FORTRAN Subroutines for Testing Unconstrained Optimization Software [C5], [E4] , 1981, TOMS.

[12]  Navid Asadizanjani,et al.  Counterfeit Electronics Detection Using Image Processing and Machine Learning , 2017 .

[13]  R. P. Narmadha,et al.  Detection and measurement of paddy leaf disease symptoms using image processing , 2017, 2017 International Conference on Computer Communication and Informatics (ICCCI).

[14]  O. Sauer,et al.  Quasi-Newton Algorithms for Medical Image Registration , 2009 .

[15]  Issam A. R. Moghrabi,et al.  Alternative parameter choices for multi-step Quasi-Newton methods , 1993 .