Search results for " Automatic segmentation"

showing 2 items of 2 documents

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

2017

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…

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 & neurosurgery
researchProduct

A fully automatic 2D segmentation method for uterine fibroid in MRgFUS treatment evaluation

2015

PurposeMagnetic Resonance guided Focused UltraSound (MRgFUS) represents a non-invasive surgical approach that uses thermal ablation to treat uterine fibroids. After the MRgFUS treatment, an operator must manually segment the treated fibroid areas to evaluate the NonPerfused Volume (NPV). This manual approach is operator-dependent, introducing issues of result reproducibility, which could lead to errors in the subsequent follow-up phase. Moreover, manual segmentation is time-consuming, and can have a negative impact on the optimization of both machine-time and operator-time. MethodTo address these issues, in this paper a novel fully automatic method based on the unsupervised Fuzzy C-Means cl…

medicine.medical_specialtyDatabases FactualUterine fibroidsComputer scienceAdaptive thresholdingImage ProcessingAdaptive thresholding; Automatic segmentation; Fuzzy C-Means clustering; MRgFUS treatment; Uterine fibroids; Female; Humans; Image Processing Computer-Assisted; Leiomyoma; Radiography; Algorithms; Databases Factual; Magnetic Resonance Imaging; Ultrasonography InterventionalHealth InformaticsFuzzy logicDatabasesComputer-AssistedImage Processing Computer-AssistedmedicineHumansSegmentationCluster analysisFactualUltrasonography InterventionalUltrasonographySettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniInterventionalLeiomyomaPixelbusiness.industryPattern recognitionmedicine.diseaseMagnetic Resonance ImagingFuzzy C-Means clusteringComputer Science ApplicationsSurgeryRadiographyTreatment evaluationMRgFUS treatmentFully automaticFemaleManual segmentationArtificial intelligenceAutomatic segmentationAdaptive thresholding Automatic segmentation Fuzzy C-Means clustering MRgFUS treatment Uterine fibroidsbusinessAlgorithmsUterine fibroids
researchProduct