A Selective Segmentation Model Using Dual-Level Set Functions and Local Spatial Distance

• Published in: IEEE access Vol. 10; pp. 22344 - 22358
• Main Authors: Rahman, Afzal, Ali, Haider, Badshah, Noor, Rada, Lavdie, Khan, Ayaz Ali, Hussain, Hameed, Zakarya, Muhammad, Ahmed, Aftab, Rahman, Izaz Ur, Raza, Mushtaq, Haleem, Muhammad
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Active contours; Background noise; Computational modeling; Edge detection; Euler-Lagrange equation; Homogeneity; Image edge detection; Image segmentation; Level set; level set function; local similarity factor; local spatial distance; Mathematical models; Medical imaging; Motion segmentation; Robustness; selective segmentation
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3152785

Selective image segmentation is one of the most significant subjects in medical imaging and real-world applications. We present a robust selective segmentation model based on local spatial distance utilizing a dual-level set variational formulation in this study. Our concept tries to partition all objects using a global level set function and the selected item using a different level set function (local). Our model combines the marker distance function, edge detection, local spatial distance, and active contour without edges into one. The new model is robust to noise and gives better performance for images having intensity in-homogeneity (background and foreground). Moreover, we observed that the proposed model captures objects which do not have uniform features. The experimental results show that our model is robust to noise and works better than the other existing models.