Fully automatic multispectral MR image segmentation of prostate gland based on the fuzzy C-means clustering algorithm

Salvatore Vitabile, Maria Carla Gilardi, Giorgio Russo, Carmelo Militello, Leonardo Rundo, Davide D’Urso, Antonio Garufi, Giancarlo Mauri, Lucia Maria Valastro, Carmelo Militello

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationMultidisciplinary Approaches to Neural Computing
Pages23-37
Number of pages15
Publication statusPublished - 2018

Publication series

NameSMART INNOVATION, SYSTEMS AND TECHNOLOGIES

All Science Journal Classification (ASJC) codes

  • General Decision Sciences
  • General Computer Science

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