Kidney segmentation from DCE-MRI converging level set methods, fuzzy clustering and Markov random field modeling

• Published in: Scientific reports Vol. 12; no. 1; pp. 18816 - 11
• Main Authors: El-Melegy, Moumen, Kamel, Rasha, El-Ghar, Mohamed Abou, Shehata, Mohamed, Khalifa, Fahmi, El-Baz, Ayman
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 05.11.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/985; 639/166/987; 692/700/1421/1770; Accuracy; Cluster Analysis; Humanities and Social Sciences; Humans; Image processing; Image Processing, Computer-Assisted - methods; Kidney - diagnostic imaging; Kidney transplantation; Kidneys; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; multidisciplinary; Neural networks; Noise levels; Science; Science (multidisciplinary); Segmentation

Early diagnosis of transplanted kidney function requires precise Kidney segmentation from Dynamic Contrast-Enhanced Magnetic Resonance Imaging images as a preliminary step. In this regard, this paper aims to propose an automated and accurate DCE-MRI kidney segmentation method integrating fuzzy c-mea...