TY - CHAP
T1 - Fully automatic multispectral MR image segmentation of prostate gland based on the fuzzy C-means clustering algorithm
AU - Vitabile, Salvatore
AU - Gilardi, Maria Carla
AU - Russo, Giorgio
AU - Militello, Carmelo
AU - Rundo, Leonardo
AU - D’Urso, Davide
AU - Garufi, Antonio
AU - Mauri, Giancarlo
AU - Valastro, Lucia Maria
AU - Militello, Carmelo
PY - 2018
Y1 - 2018
N2 - Prostate imaging is a very critical issue in the clinical practice, especially for diagnosis, therapy, and staging of prostate cancer. Magnetic Resonance Imaging (MRI) can provide both morphologic and complementary functional information of tumor region. Manual detection and segmentation of prostate gland and carcinoma on multispectral MRI data is not easily practicable in the clinical routine because of the long times required by experienced radiologists to analyze several types of imaging data. In this paper, a fully automatic image segmentation method, exploiting an unsupervised Fuzzy C-Means (FCM) clustering technique for multispectral T1-weighted and T2-weighted MRI data processing, is proposed. This approach enables prostate segmentation and automatic gland volume calculation. Segmentation trials have been performed on a dataset composed of 7 patients affected by prostate cancer, using both area-based and distance-based metrics for its evaluation. The achieved experimental results are encouraging, showing good segmentation accuracy.
AB - Prostate imaging is a very critical issue in the clinical practice, especially for diagnosis, therapy, and staging of prostate cancer. Magnetic Resonance Imaging (MRI) can provide both morphologic and complementary functional information of tumor region. Manual detection and segmentation of prostate gland and carcinoma on multispectral MRI data is not easily practicable in the clinical routine because of the long times required by experienced radiologists to analyze several types of imaging data. In this paper, a fully automatic image segmentation method, exploiting an unsupervised Fuzzy C-Means (FCM) clustering technique for multispectral T1-weighted and T2-weighted MRI data processing, is proposed. This approach enables prostate segmentation and automatic gland volume calculation. Segmentation trials have been performed on a dataset composed of 7 patients affected by prostate cancer, using both area-based and distance-based metrics for its evaluation. The achieved experimental results are encouraging, showing good segmentation accuracy.
UR - http://hdl.handle.net/10447/280205
UR - http://www.springer.com/series/8767
M3 - Chapter
SN - 978-3-319-56903-1
T3 - SMART INNOVATION, SYSTEMS AND TECHNOLOGIES
SP - 23
EP - 37
BT - Multidisciplinary Approaches to Neural Computing
ER -