Uterine Segmentation and Volume Measurement in Uterine Fibroid Patients’ MRI Using Fuzzy C-Mean Algorithm and Morphological Operations


avatar Alireza Fallahi 1 , avatar Mohammad Pooyan 2 , avatar Hassan Hashemi 2 , avatar Hossein Ghanaati 2 , avatar Mohammad Oghabian 2 , avatar Hassan Khotanlou 1 , avatar Madjid Shakiba 2 , avatar Amir Jalali 2 , avatar Kavous Firouznia 3 , *

Department of Biomedical Engineering, Hamedan University of Technology, Hamedan, Iran
Department of Biomedical Engineering, Shahed University, Tehran, Iran
Department of Radiology, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran

how to cite: Fallahi A, Pooyan M, Hashemi H, Ghanaati H, Oghabian M, et al. Uterine Segmentation and Volume Measurement in Uterine Fibroid Patients’ MRI Using Fuzzy C-Mean Algorithm and Morphological Operations. Innov J Radiol. 2011;8(3):e93569. https://doi.org/10.5812/kmp.iranjradiol.17351065.3142.


Background: Uterine fibroids are common benign tumors of the female pelvis. Uterine artery embolization (UAE) is an effective treatment of symptomatic uterine fibroids by shrinkage of the size of these tumors. Segmentation of the uterine region is essential for an accurate treatment strategy. Objectives: In this paper, we will introduce a new method for uterine segmentation in T1W and enhanced T1W magnetic resonance (MR) images in a group of fibroid patients candidated for UAE in order to make a reliable tool for uterine volumetry. Patients and Methods: Uterine was initially segmented using Fuzzy C-Mean (FCM) method in T1W-enhanced images and some morphological operations were then applied to refine the initial segmentation. Finally redundant parts were removed by masking the segmented region in T1W-enhanced image over the registered T1W image and using histogram thresholding. This method was evaluated using a dataset with ten patients’ images (sagittal, axial and coronal views). Results: We compared manually segmented images with the output of our system and obtained a mean similarity of 80%, mean sensitivity of 75.32% and a mean specificity of 89.5%. The Pearson correlation coefficient between the areas measured by the manual method and the automated method was 0.99. Conclusions: The quantitative results illustrate good performance of this method. By uterine segmentation, fibroids in the uterine may be segmented and their properties may be analyzed.



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