NEURAL NETWORK BASED LEARNING KERNEL FOR AUTOMATIC SEGMENTATION OF MULTIPLE SCLEROSIS LESIONS ON MAGNETIC RESONANCE IMAGES

چکیده مقاله

ABSTRACT Background: Multiple Sclerosis (MS) is a degenerative dieses of central nervous system. MS patients have some dead tissues in their brain which is called MS lesions. MRI is an imaging technique sensitive to soft tissues such as brain that shows MS lesions as hyper-intense or hypo-intense signals. As manual segmentation of these lesions is a laborious and time consuming task, automatic segmentation is a need. Materials and Methods: In order to segment MS lesions a method based on learning kernels has been proposed. The proposed method has three main steps namely pre-processing, sub-region extraction, and segmentation. The segmentation is performed by a kernel. This kernel is trained using a modified version of a special type of Artificial Neural Networks (ANN) called Massive Training ANN (MTANN). The kernel incorporate surrounding pixels information as features for classification of middle pixel of kernel. The materials of this study are a part of MICCAI 2008 MS lesion segmentation grand challenge data-set. Results: Both qualitative and quantitative results show promising results. Similarity index of 70 percent of some cases is considered as good. This results are obtained from information of only one MRI channel rather than multi-channel MRIs. Conclusion: This study show the potential of surrounding pixels information to be incorporated in segmentation by learning kernels. The performance of proposed method will be improved by using a special pre-processing pipeline and also a post-processing step for reducing false positives/negatives. An important advantage of proposed model is that it use just FLAIR MRI that reduce computational time and brings comfort for patients.