Normalized level set model for segmentation of low-contrast objects in 2- and 3-dimensional images

Mirza M. Junaid Baig; Yao L. Wang; Samuel H. Chung; Armen Stepanyants

doi:10.1117/12.3018402

12 March 2024 Normalized level set model for segmentation of low-contrast objects in 2- and 3-dimensional images

Mirza M. Junaid Baig, Yao L. Wang, Samuel H. Chung, Armen Stepanyants

Proceedings Volume 12848, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXXI; 128480E (2024) https://doi.org/10.1117/12.3018402
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

Analyses of biomedical images often rely on accurate segmentation of structures of interest. Traditional segmentation methods based on thresholding, watershed, fast marching, and level set perform well in high-contrast images containing structures of similar intensities. However, such methods can under-segment or miss entirely low-intensity objects on noisy backgrounds. Machine learning segmentation methods promise superior performance but require large training datasets of labeled images which are difficult to create, particularly in 3D. Here, we propose an algorithm based on the Local Binary Fitting level set method and its application specifically designed to improve the segmentation accuracy for low-contrast structures even with significant noise levels present. The proposed algorithm, the Normalized Local Binary Fitting level set method, shows promise in enhancing the segmentation of low-contrast structures in biomedical images, addressing the limitations of traditional segmentation methods, and offering an alternative to machine learning approaches that require extensive training datasets.

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

Citation Download Citation

Mirza M. Junaid Baig, Yao L. Wang, Samuel H. Chung, and Armen Stepanyants "Normalized level set model for segmentation of low-contrast objects in 2- and 3-dimensional images", Proc. SPIE 12848, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXXI, 128480E (12 March 2024); https://doi.org/10.1117/12.3018402

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