Fetal head segmentation based on Gaussian elliptical path optimize by flower pollination algorithm and cuckoo search

Number of maternal and infant mortality in Indonesia is high. This problem can be minimized by monitoring the fetal condition via ultrasound image. In addition, Indonesia have small number of obstetrics and gynecology compare to number of its population. Moreover, it is centralized in urban areas, so it is hard to monitor the condition of every babies in Indonesia. In order to resolve this problem, we have built fetal head monitoring system. Part of the system is to segment the fetal head in ultrasound image. In this paper, we examine nature optimization such as bat algorithm, cuckoo search, and flower pollination algorithm for optimizing Gaussian elliptical path for automatic fetal head segmentation. Experiment results shows that nature optimization Based Gaussian elliptical path (DoGEII-FPA and DoGEII-CS) has a minimum error compared to Gaussian elliptical path (DoGEll) which is optimized by Nelder-Mead. Interestingly, DoGEll-FPA and DoGEll-CS perform well from DoGEll-NM in different image.

[1]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[2]  M. Anwar Ma'sum,et al.  Automatic fetal head approximation using Particle Swarm Optimization based Gaussian Elliptical Path , 2015, 2015 International Symposium on Micro-NanoMechatronics and Human Science (MHS).

[3]  Alessandro Foi,et al.  Difference of Gaussians revolved along elliptical paths for ultrasound fetal head segmentation , 2014, Comput. Medical Imaging Graph..

[4]  M. Anwar Ma'sum,et al.  Automatic fetal organs detection and approximation in ultrasound image using boosting classifier and hough transform , 2014, 2014 International Conference on Advanced Computer Science and Information System.

[5]  M. A. Ma'sum,et al.  Automated Telehealth System for Fetal Growth Detection and Approximation of Ultrasound Images , 2015 .

[6]  Xin-She Yang,et al.  Engineering optimisation by cuckoo search , 2010, Int. J. Math. Model. Numer. Optimisation.

[7]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[8]  LU Qiu-qin.  Bat algorithm with global convergence for solving large-scale optimization problem , 2013 .

[9]  Toshio Fukuda,et al.  Fuzzy learning vector quantization based on particle swarm optimization for artificial odor dicrimination system , 2009 .

[10]  Xin-She Yang,et al.  Flower Pollination Algorithm for Global Optimization , 2012, UCNC.

[11]  Xin-She Yang,et al.  Multi-Objective Flower Algorithm for Optimization , 2014, ICCS.

[12]  Xin-She Yang,et al.  Engineering optimisation by cuckoo search , 2010 .

[13]  Toshio Fukuda,et al.  Localizing multiple odor sources in a dynamic environment based on modified niche particle swarm optimization with flow of wind , 2009 .

[14]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[15]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .