Multiple sclerosis segmentation method in magnetic resonance imaging using fuzzy connectedness, binarization, mathematical morphology, and 3D reconstruction

Abstract

Abstract Introduction Magnetic resonance imaging (MRI) is the most used medical modality for diagnosis and monitoring of multiple sclerosis (MS). A segmentation process is an important task to quantify lesion and its progression. However, manual segmentation of 3D images is tedious, time-consuming, and often not reproducible. The state of the art presents results with room for improvements. Consequently, a semiautomatic segmentation process is proposed and described in this study. Methods The method consists on a 3D segmentation semiautomatic process for MS lesions in MRI. It initiates by firstly carrying out a preprocessing stage; thus, contrast adjustment is applied to enhance sclerosis regions from other brain information. Secondly, a feature extraction block based on fuzzy connectedness is performed so as to isolate sclerosis lesions from other brain regions. Finally, 3D brain reconstruction is executed along with sclerosis to provide a useful 3D information. Results The robustness of this approach is demonstrated by high correlation between the results and their corresponding gold standard. The results were also obtained by computing parameters of accuracy of image segmentation, as well as overlap Dice. The proposed method reached true positive of 75.61%, false positive of 16.37%, and DICE of 78.23%. Conclusion The high correlation between specialist and proposed approach outcome, a better monitoring of the disease, is provided; the specialist can understand the patient’s symptoms, thereby increasing the patient’s quality of life.