Online ISSN: 2515-8260

Author : BabyKalpana, Dr.Y.


AUTOMATIC SEGMENTATION OF SPINAL CORD IN MRI IMAGES VIA ITERATIVE CUCKOO SEARCH BASED RANDOM WALKER AND ONLINE KERNEL LEARNING (OKL) CLASSIFIER

Dr. D. Brindha; N.R. Deepa; Dr.Y. BabyKalpana; Dr.K. Murugan; Dr.A. Devipriya

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 875-891

Segmentation of Spinal Cord (SC) part has a major role in assessing spinal cord atrophy.
Spinal Cord segmentation is not similar to that of brain segmentation, although Magnetic Resonance
Imaging (MRI) sequence are greatly deployed for spinal cord examination. There is always a great
challenge in spinal cord MRI segmentation which has been investigated by many researches. Also,
considerable accuracy and degree of complexity for segmentation have been presented and elucidated in
prevailing researches. A new approach namely combining Iterative Random-Walk (RW) solver and a
Cuckoo Search Algorithm (CSA) has been suggested, thus facilitating direct homogenous and
heterogeneous SC measurements comparison. An interactive RW solver with CSA is greatly utilized for
complete cascaded pipelining in automatic manner. The initialization of automatic segmentation pipeline is
done through powerful voxelwise classification via Online Kernel Learning (OKL) classifier. Therefore,
SC topology refinement is done iteratively along with cascading of RW-CSA solvers for attaining proper
segmentation outcomes in less iteration, even for cases including bone fractures and lesions. The
segmentation experimental outcome mainly relies on MRI images indicating achievement of improved
accuracy when compared with prevailing approaches