Work

A hybrid variational level set model based on double Gaussian distribution fitting energy for image segmentation and bias correction

Year: 2024

Type: article

Abstract: The variational level set model has been widely used in image segmentation. However, its performance is significantly hindered by bias fields and noise within the images. To address these limitations, we introduce a novel hybrid variational level set model based on dual Gaussian distribution fitting (DGDF) energy in this paper. The DGDF energy integrates both local and global Gaussian distributions. The local energy is derived from the original image, while the global energy employs the corrected image, enabling effective segmentation of images with intensity inhomogeneity. Furthermore, the model demonstrates low sensitivity to weighting parameters and robust performance for noisy images. We develop an alternating iteration algorithm that combines variational methods with gradient descent to efficiently solve the proposed model. Experimental results validate the effectiveness of the model and the algorithm. In addition, the proposed model shows competitiveness on test images and three datasets compared to several state‐of‐the‐art models, including other variational level set models and deep learning‐based techniques.

Source: IET Image Processing

DOI: 10.1049/ipr2.13248

Authors: Liming Tang, Honglu Zhang, Yaya Xu, Yanjun Ren, Chunyan Li

Institutions: Hubei University for Nationalities, Enshi Polytechnic, Chongqing University of Science and Technology

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,610

Funders: National Natural Science Foundation of China, Natural Science Foundation of Chongqing