Brain MR Image Lesion Identification Using Threshold-Based Segmentation Techniques

摘要

Brain stroke is the major second leading cause of death for the people above the age of 60 and fifth leading cause in people aged 15-59. Automatic ischemic stroke lesion segmentation of Magnetic Resonance Images (MRI) is an important task since manual identification by medical experts is time-consuming. In this work, the Diffusion Weighted (DW) brain MR images of ISLES 2015 challenge dataset and Radiopaedia are pre-processed based on hybrid contrast enhancement and filtering technique. The skull is stripped by thresholding techniques along with mathematical morphology procedure. Later the identification of ischemic stroke lesion is performed by maximum entropy-based and Fuzzy C Means (FCM) thresholding. The segmented results are compared with expert's ground truth and the performance is analyzed by means of similarity indices. It is observed that FCM performs better in identifying multiple and minute stroke lesion in MR images. There is nearly 98% close match between FCM segmented results and the ground truth scribed by the experts. Thus the proposed automated work flow helps in assisting the clinicians to identify the ischemic stroke lesion in DW modality of brain MR images.