Title: Enhancement of fissure using back propagation neural network and segmentation of lobes in CT scan image

Authors: K.K. Thanammal; J.S. Jaya Sudha

Addresses: Department of MCA, S.T. Hindu College, Kottar, Nagercoil, Tamil Nadu, India ' Department of Computer Science and Engineering, Sree Chitra Thirunal College of Engineering, Pappanamcode, Thiruvananthapuram, Kerala, India

Abstract: Computed tomography technology is one of the most efficient techniques, which shows the inside parts of the human body through scanning the specific area. The CT image normally shows the detailed information of the lungs, which is used for surgical planning. Segmentation of lobes from lung is a very challenging task in CT scan image when the abnormalities or anomalies are presented in the lung image. There are various problems in the fissure enhancement process such as incomplete fissure, partially seen fissure, etc. Back Propagation Neural Network (BPNN) is used for fissure enhancement process. By using the fissure enhanced image, the lobe is segmented using canny edge detection method. Neural Network is used as a non-linear filter and it is trained using Back Propagation algorithm for the enhancement of fissure. Simulation results show that accuracy is improved for segmentation of lobes from lungs.

Keywords: computed tomography; CT scan images; threshold; lung segmentation; back propagation; neural networks; lobe segmentation; fissure enhancement; image enhancement; lung scanning; canny edge detection; nonlinear filter; simulation; lungs; image processing; medical imaging.

DOI: 10.1504/IJBET.2016.074108

International Journal of Biomedical Engineering and Technology, 2016 Vol.20 No.1, pp.1 - 11

Received: 04 Sep 2014
Accepted: 10 Feb 2015

Published online: 11 Jan 2016 *

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