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Information 2017, 8(2), 49; doi:10.3390/info8020049

Automated Prostate Gland Segmentation Based on an Unsupervised Fuzzy C-Means Clustering Technique Using Multispectral T1w and T2w MR Imaging

1
Dipartimento di Informatica, Sistemistica e Comunicazione (DISCo), Università degli Studi di Milano-Bicocca, Viale Sarca 336, Milano 20126, Italy
2
Istituto di Bioimmagini e Fisiologia Molecolare-Consiglio Nazionale delle Ricerche (IBFM-CNR), Contrada Pietrapollastra-Pisciotto, Cefalù (PA) 90015, Italy
3
Azienda Ospedaliera per l’Emergenza Cannizzaro, Via Messina 829, Catania 95126, Italy
4
Dipartimento di Biopatologia e Biotecnologie Mediche (DIBIMED), Università degli Studi di Palermo, Via del Vespro 129, Palermo 90127, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Giovanna Castellano, Laura Caponetti and Willy Susilo
Received: 4 February 2017 / Revised: 3 April 2017 / Accepted: 24 April 2017 / Published: 28 April 2017
(This article belongs to the Special Issue Fuzzy Logic for Image Processing)
View Full-Text   |   Download PDF [4264 KB, uploaded 28 April 2017]   |  

Abstract

Prostate imaging analysis is difficult in diagnosis, therapy, and staging of prostate cancer. In clinical practice, Magnetic Resonance Imaging (MRI) is increasingly used thanks to its morphologic and functional capabilities. However, manual detection and delineation of prostate gland on multispectral MRI data is currently a time-expensive and operator-dependent procedure. Efficient computer-assisted segmentation approaches are not yet able to address these issues, but rather have the potential to do so. In this paper, a novel automatic prostate MR image segmentation method based on the Fuzzy C-Means (FCM) clustering algorithm, which enables multispectral T1-weighted (T1w) and T2-weighted (T2w) MRI anatomical data processing, is proposed. This approach, using an unsupervised Machine Learning technique, helps to segment the prostate gland effectively. A total of 21 patients with suspicion of prostate cancer were enrolled in this study. Volume-based metrics, spatial overlap-based metrics and spatial distance-based metrics were used to quantitatively evaluate the accuracy of the obtained segmentation results with respect to the gold-standard boundaries delineated manually by an expert radiologist. The proposed multispectral segmentation method was compared with the same processing pipeline applied on either T2w or T1w MR images alone. The multispectral approach considerably outperforms the monoparametric ones, achieving an average Dice Similarity Coefficient 90.77 ± 1.75, with respect to 81.90 ± 6.49 and 82.55 ± 4.93 by processing T2w and T1w imaging alone, respectively. Combining T2w and T1w MR image structural information significantly enhances prostate gland segmentation by exploiting the uniform gray appearance of the prostate on T1w MRI. View Full-Text
Keywords: automated segmentation; multispectral MR imaging; prostate gland; prostate cancer; unsupervised Machine Learning; Fuzzy C-Means clustering automated segmentation; multispectral MR imaging; prostate gland; prostate cancer; unsupervised Machine Learning; Fuzzy C-Means clustering
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Rundo, L.; Militello, C.; Russo, G.; Garufi, A.; Vitabile, S.; Gilardi, M.C.; Mauri, G. Automated Prostate Gland Segmentation Based on an Unsupervised Fuzzy C-Means Clustering Technique Using Multispectral T1w and T2w MR Imaging. Information 2017, 8, 49.

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