A medical image segmentation approach based on ULFC-MSPCNN

Qin Zhou; Na Zhang; Jinyu Zhang; Yuzhu Cao; Jing Lian

doi:10.1117/12.3035154

19 July 2024 A medical image segmentation approach based on ULFC-MSPCNN

Qin Zhou, Na Zhang, Jinyu Zhang, Yuzhu Cao, Jing Lian

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132130W (2024) https://doi.org/10.1117/12.3035154
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

To address the issue of low segmentation accuracy and high computational complexity in traditional pulse-coupled neural network (PCNN) for medical image processing, this paper proposes an image segmentation approach based on unit-linking fire-controlled multi-scale pulse-coupled neural network (ULFC-MSPCNN). This method simplifies the configuration of data parameters and reduces the number of iterations during the effective transmission period. Experimental results demonstrate that the proposed method significantly reduces detection costs while improving detection accuracy. Moreover, it notably diminishes the randomness and unpredictability of discharged neurons. Thus, it can be seen as one of the effective approaches for medical image segmentation.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qin Zhou, Na Zhang, Jinyu Zhang, Yuzhu Cao, and Jing Lian "A medical image segmentation approach based on ULFC-MSPCNN", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132130W (19 July 2024); https://doi.org/10.1117/12.3035154

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available