6533b870fe1ef96bd12cfb0a
RESEARCH PRODUCT
Fully automatic multispectral MR image segmentation of prostate gland based on the fuzzy C-means clustering algorithm
Giorgio Ivan RussoSalvatore VitabileDavide D’ursoGiancarlo MauriL. M. ValastroAntonio GarufiCarmelo MilitelloLeonardo RundoMaria Carla Gilardisubject
Computer scienceMultispectral imageFully automatic segmentation; Multispectral MR imaging; Prostate cancer; Prostate gland; Unsupervised fuzzy C-means clusteringFuzzy logic030218 nuclear medicine & medical imaging03 medical and health sciencesProstate cancer0302 clinical medicineProstatemedicineSegmentationComputer visionCluster analysismedicine.diagnostic_testbusiness.industryINF/01 - INFORMATICAMagnetic resonance imagingfully automatic segmentationImage segmentationmedicine.diseaseprostate cancermultispectral MR imagingunsupervised Fuzzy C-Means clusteringmedicine.anatomical_structureArtificial intelligencebusinessprostate gland030217 neurology & neurosurgerydescription
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.
year | journal | country | edition | language |
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2017-01-01 |