Title: Detection of computationally-intensive functions in a medical image segmentation algorithm based on an active contour model

Authors: Carlos A.S.J. Gulo; Antonio C. Sementille; João Manuel R.S. Tavares

Addresses: CNPq National Scientific and Technological Development Council, Research Group PIXEL – UNEMAT, Brazil; Programa Doutoral em Engenharia Informática, Universidade do Porto, Portugal ' Departamento de Ciências da Computação, Faculdade de Ciências, Universidade Estadual Paulista – UNESP, Brazil ' Departamento de Engenharia Mecânica, Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Universidade do Porto, Portugal

Abstract: Image segmentation is one of the most critical operations performed on medical images. These operations require developing optimisation strategies to reduce runtime. Profiling methods can assess algorithm's performance concerning the overall cost of runtime, memory access, and performance bottlenecks. Therefore, we propose an approach for detecting computationally intensive functions in a competent medical image segmentation algorithm based on an active contour model. Our approach applies performance analysis tools commonly available in traditional computer operating systems, requiring no new setup or developing new performance-measuring techniques. The overall cost of execution time, memory accesses, and performance bottlenecks are measured in execution time. In conclusion, a call graph visualisation can suggest to users a quick graphical overview of the execution time of their codes and, therefore, guarantee the shortest possible learning curve by the community of researchers from medical image processing and analysis.

Keywords: medical image processing and analysis; profiling tools; performance analysis; high-performance computing.

DOI: 10.1504/IJCSE.2023.133682

International Journal of Computational Science and Engineering, 2023 Vol.26 No.5, pp.555 - 566

Received: 21 Nov 2021
Received in revised form: 16 May 2022
Accepted: 25 May 2022
Published online: 29 Sep 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article