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

Risultato della ricerca: Chapter

4 Citazioni (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.
Lingua originaleEnglish
Titolo della pubblicazione ospiteMultidisciplinary Approaches to Neural Computing
Pagine23-37
Numero di pagine15
Stato di pubblicazionePublished - 2018

Serie di pubblicazioni

NomeSMART INNOVATION, SYSTEMS AND TECHNOLOGIES

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Computer Science(all)

Fingerprint Entra nei temi di ricerca di 'Fully automatic multispectral MR image segmentation of prostate gland based on the fuzzy C-means clustering algorithm'. Insieme formano una fingerprint unica.

Cita questo