We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Automatic Detection of Liver in CT images using Optimal Feature based Neural Network

by Ritu Punia, Shailendra Singh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 76 - Number 15
Year of Publication: 2013
Authors: Ritu Punia, Shailendra Singh
10.5120/13326-0903

Ritu Punia, Shailendra Singh . Automatic Detection of Liver in CT images using Optimal Feature based Neural Network. International Journal of Computer Applications. 76, 15 ( August 2013), 53-60. DOI=10.5120/13326-0903

@article{ 10.5120/13326-0903,
author = { Ritu Punia, Shailendra Singh },
title = { Automatic Detection of Liver in CT images using Optimal Feature based Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 76 },
number = { 15 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 53-60 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume76/number15/13326-0903/ },
doi = { 10.5120/13326-0903 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:46:01.188850+05:30
%A Ritu Punia
%A Shailendra Singh
%T Automatic Detection of Liver in CT images using Optimal Feature based Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 76
%N 15
%P 53-60
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a method for automatic detection of liver in CT images using optimal texture features. As image contains noise so firstly, image is pre-processed with median filter. Regions of interests are chosen carefully from both liver and non-liver areas. Texture features are extracted from selected regions of interest using first order statistics and wavelet transform. Neural Network is used for classification of pixels into liver and non-liver areas. Accuracy of classification process depends on number of features extracted which should be chosen carefully. For careful selection of features, optimal features are selected from extracted features using genetic algorithm and used for final classification of image pixels. The method is tested on CT images and results obtained are presented both qualitatively and quantitatively.

References
  1. Tsai, D. , Tanahashi, N. 1994. Neural-network-based boundary detection of liver structure in CT images for 3D visualization. In Proceedings of IEEE international conference on neural networks. Vol. 6 (1). 3484-89.
  2. Koss, J. E. , Newmann, F. D. , Johnson, T. K. , Kirch, D. L. 1999. Abdominal organ segmentation using texture transforms and a hopfield neural network. IEEE Trans Med Imaging. Vol. 18(7). 640–648.
  3. Haralick, R. M. 1979. Statistical and Structural Approaches to Texture, Proc. IEEE. Vol. 67(5). 786-806.
  4. Hussain S. A. , Shigeru E. 2000. Use of neural network for feature Based Recognition Of Liver Region On CT Images. In proceedings of IEEE Signal Processing Society Workshop. Vol. 2. 831-840.
  5. Rafiee, A. , Masoumi, H. , Roosta, A. 2009. Using neural network for liver detection in abdominal MRI images. In International conference on signal and image processing applications IEEE. 21-26.
  6. Masoumi, H. , Behrad, A. , Ali Pourmina, M. , Roosta, A. 2012. Automatic Liver Segmentation in MR images using an iterative watershed algorithm and artificial neural network. In Biomedical signal processing and control Elsevier. 429-437.
  7. Laine, A. , Fan, J. 1993. Texture Classification by Wavelet Packet Signatures. IEEE Transactions Pattern Analysis and Machine Intelligence. Vol. 15(11). 1186-1191.
  8. Luo, S. , Hu, Q. , He, X. , Li, J. , Jin, J. S. , Park, M. 2009. Automatic Liver Parenchyma Segmentation from Abdominal CT Images Using Support Vector Machines. International Conference on Complex Medical engineering, IEEE.
  9. Luo, S. , Jin, J. S. , Chalup, S. K. , Qian, G. 2009. A Liver Segmentation Algorithm Based on Wavelets and Machine Learning. International Conference on Computational Intelligence and Natural Computing, IEEE.
  10. Lu, J. , Wang, D. , Shi, L. , Ann Heng, P. 2012. Automatic Liver Segmentation in CT images based on Support Vector Machine In proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics. 333-336.
  11. Chi, D. , Zhao, Y. , Li, M. 2010. Automatic Liver MR Image Segmentation with Self Organizing Map and Hierarchical Agglomerative Clustering Method. In 3rd International Congress on Image and signal processing, IEEE. 1333-1337.
  12. Gonzalez, Rafael C. , Woods, Richard E. Digital Image Processing. Third Edition. Pearson Education.
  13. Materka, A. , Strzelecki, M. Texture Analysis Methods – A Review. Technical University of Lodz, Institute of Electronics, Brussels.
  14. Mallat, S. 1989. Multifrequency Channel Decomposition of Images and Wavelet Models, IEEE Trans. Acoustic, Speech and Signal Processing. Vol 37(12) 2091-2110.
  15. Sebe, N. , Lew, M. S. 2000. Wavelet Based Texture Classification. In 15th International Conference on Pattern Recognition. Vol. 3. 947-950.
  16. Yang, J. , Honavar, V. 1998. Feature Subset Selection using a Genetic Algorithm. Intelligent Systems and their Applications, IEEE. Vol. 13(2). 44-49.
  17. http://www. google. co. in/imghp?hl=en&tab=wi
Index Terms

Computer Science
Information Sciences

Keywords

Liver Detection Texture Optimal Features Genetic Algorithm Neural Network

Powered by PhDFocusTM