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Segmentation of MRI data using multi-objective antlion based improved fuzzy c-means

Singh Munendra, Venkatesh Vishal, Verma Ashish, Sharma Neeraj

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 3

1250--1266

Accurate segmentation of brain tissues in magnetic resonance imaging (MRI) data plays critical role in the clinical diagnostic and treatment planning. The presence of noise and artifacts in MRI data degrades the performance of segmentation algorithms. In this view, the present study proposes a complete unsupervised clustering based multi-objective modified fuzzy c-mean (MOFCM) segmentation algorithm, which inculcates multi-objective antlion optimization (MOALO) to minimize the cluster compactness and fuzzy hyper-volume fitness functions. The output segmented image corresponds to minimum value of partition entropy in the obtained solution set. The present study integrates proposed MOFCM with a new cluster number validity index, which allows user not to provide number of segments in image as an input. The proposed MOFCM algorithm is extensively validated on seventy two synthetic images corrupted with different levels of Gaussian, Speckle and Rician noises, forty simulated BrainWeb MRI images suffered from noise and inhomogeneity, and 10 real IBSR MRI dataset of images. The results are compared with existing popular clustering based algorithms, and supervised deep learning based algorithms, i.e. UNet, SegNet and Quick- NAT. The proposed MOFCM algorithm demonstrate the superior segmentation performance in comparison to popular FCM based clustering algorithms, SegNet and UNet, whereas the segmentation results of proposed MOFCM are at par with QuickNAT.

On wire spark erosion machining induced surface integrity of Ni55.8Ti shape memory alloys

Sharma Neeraj, Gupta Kapil, Davim Joao Paulo

Archives of Civil and Mechanical Engineering

2019

Vol. 19, no. 3

680--693

Ni55.8Ti shape memory alloys (SMAs) find applications in different fields of medical and engineering. In every field, surface integrity greatly affects the functional performance of shape memory alloy parts. In the present work, wire spark erosion machining of Ni55.8Ti shape memory alloys has been conducted and surface integrity parameters of the machined specimens have been evaluated. Experiments are designed using Taguchi L16 robust design of experiment technique. Effect of important process parameters, i.e. voltage, pulse-on time and pulse-off time on maximum surface roughness has been studied. Deterioration in surface integrity at various combinations of pulse-on and pulse-of time which produced high discharge energy has been observed. Scanned electron microscopic investigation, energy dispersive spectroscopy and XRD analyses, roughness measurement, and micro-hardness testing results are presented, analyzed and discussed. Optimization of process parameters resulted in surface integrity enhancement with low roughness (Rt – 7.78 mm and Ra – 1.45 mm) and very thin recast layer (4–6 mm) along with minimum subsurface defects.