A Hybrid Segmentation Approach to Diagnose Suspicious Pixel regions in Liver CT Images

Authors

  • Jayanthi Muthuswamy  Electronics and Communication Engineering, New Horizon College of Engineering, Bangalore, India
  • Divya Sagar Reddy  Electronics and Communication Engineering, New Horizon College of Engineering, Bangalore, India
  • Madala Himaja  Electronics and Communication Engineering, New Horizon College of Engineering, Bangalore, India
  • Bhaskar K  Electronics and Communication Engineering, New Horizon College of Engineering, Bangalore, India
  • Aravind  Electronics and Communication Engineering, New Horizon College of Engineering, Bangalore, India
  • Dhinakaran S  Electronics and Communication Engineering, New Horizon College of Engineering, Bangalore, India

Keywords:

Bilateral filter, Neutromatic logic, Fuzzy C means, Thresholding, morphological operation.

Abstract

This paper introduces computer aided liver analysis to diagnosis the suspicious pixel region (lesion) from abdominal CT images of liver and helps the radiologists in categorizing liver into typical or anomalous liver . Segmenting the liver and separating the region of interest is a difficult procedure in the field of malignant growth imaging because of the little recognizable changes between healthy tissues and unhealthy tissues. In this paper, segmentation of liver from abdominal CT image based on hybrid method is proposed. The method uses neutromatic logic with FCM thresholding, encouraged by pre processing using bilateral filter and post processing using morphological tasks for automatic segmentation of liver and finally dynamic thresholding and contour detection to extract the lesion (tumor). The effectiveness of proposed method is quantitatively evaluated by comparing automatic segmentation results with ground truth obtained from radiologists.

References

  1. M.Jayanthi, "Comparative Study of Different Techniques Used for medical image segmentation of Liver from Abdominal CT Scan", IEEE WiSPNET 2016 conference, pp.1462-1465,2016.
  2. Gehad Ismail Sayed, Mona Abdelbaset Ali, Tarek Gaber, Aboul Ella Hassanien and Vaclav Snasel , "A Hybrid Segmentation Approach Based on Neutrosophic Sets and Modified Watershed: A Case of Abdominal CT Liver Parenchyma",IEEE,pp.144-149,2015.
  3. Jayanthi, M.,Kanmani, B., "Extracting the Liver and Tumour from Abdominal CT Images",IEEE, Fifth International Conference on Signal and Image Processing (ICSIP), "pp. 122-125,2014.
  4. Devendra Joshi, Narendra D Londhe, " Automatic Liver Tumour Detection in Abdominal CT Images", International Journal of Computer Technology and Electronics Engineering (IJCTEE) Volume 3, Issue 1, February 2013.
  5. S. Priyadarsini and Dr.D.Selvathi, "Survey on segmentation of liver from CT images", IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), pp. 234-238, 2012.
  6. S.Gunasundari and M. Suganya Ananthi , "Comparison and Evaluation of Methods for Liver Tumor Classification from CT Dataset", International Journal of Computer Applications ,Volume 39– No.18, 2012.
  7. S.S.Kumar, R.S.Moni, I.Rajeesh, "Automatic liver and lesion segmentation: a primary step in diagnosis of liver diseases", Signal,Image and Video Processing DOl: I0.1007/s11760-011-0223-y, March 31, 2011.
  8. Hiroshi Fujita Hiroshi Fujita1, Xuejun Zhang1,2, Shoji Kido3, Takeshi Hara1, Xiangrong Zhou1, Yuji Hatanaka4, Rui Xu3, "Introduction to CAD System", International Conference on Future Computer, Control and Communication 2010, pp.200-205,2010
  9.  M Sudhamani and G. Raju, "Segmentation and Classification of Tumor in Computed Tomography Liver Images for Detection, Analysis and Preoperative Planning", International Journal of Advanced Computer Research, Vol. 4, No. 1, pp. 166-171
  10. K.Mala, V.Sadasivam, and S.Alagappan, " Neural Network based Texture Analysis of Liver Tumor from Computed Tomography Images", International Journal of Biological, Biomedical and Medical Sciences, Vol. 2 Issue 1, pp 33-37, 2007
  11. Sonali Patil, V.R. Udupi, " Preprocessing To Be Considered For MR and CT Images Containing Tumors", IOSR Journal of Electrical and Electronics Engineering, 1(4), 54-57,2012.
  12. S.S.Kumar, Dr.R.S.Moni and J.Rajeesh, " Contourlet Transform Based Computer- Aided Diagnosis System for Liver Tumor on Computed Tomography Images", International Conference on Signal Processing, Communication, Computing and Networking Technologies 2011.
  13. Ramanjot Kaur, Lakhwinder Kaur ,Savita Gupta , "Enhanced K- Mean Clustering Algorithm for Liver Image Segmentation to Extract Cyst Region",IJCA Special Issue on Novel Aspects of Digital Imaging Applications, Vol.1, pp 59-66, DIA, 2011.
  14. F. Russo, "A method for estimation and filtering of Gaussian noise in images",IEEE Transactions on Instrumentation and Measurement,vol. 52, no. 4, pp. 1148–1154, Aug. 2003 
  15.  Tomasi, C. and R. Manduchi, "Bilateral Filtering", Proceedings of the IEEE International Conference on Computer Vision,Bombay, India,pp 834-846,1998
  16. Gao, L., Heath, D., Kuszyk, B., " Automatic liver segmentation technique for three- dimensional visualization of CT data,. Radiology 201 (1996) ,359–364
  17. Bae, K., Giger, M., and Chen, C, " Automatic segmentation of liver structure in CT images " Med. Phys. 20 (1993) 71–78
  18. R. C. Gonzalez and R. E. Woods, " Digital Image Processing. Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 2006.
  19. Ming Zhang, “A novel approaches to image segmentation based on neutrosophic logic”,2010.
  20. Jayanthi Muthuswamy “Optimization based Liver Contour Extraction of Abdominal CT images ” published in International journal of Electrical and Computer Engineering (IJECE), Volume 8, Issue .6,2018.
  21. M Jayanthi, “Extraction and Classification of Liver Abnormality Based on Neutrosophic and SVM Classifier published in Progress in Advanced Computing and Intelligent Engineering,volume 1,269-279,2019.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2019-12-30

Issue

Volume 4, Issue 9, November-December-2019

Section

Research Articles

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How to Cite

[1]
Jayanthi Muthuswamy, Divya Sagar Reddy, Madala Himaja, Bhaskar K, Aravind, Dhinakaran S, " A Hybrid Segmentation Approach to Diagnose Suspicious Pixel regions in Liver CT Images " International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 9, pp.547-554, November-December-2019.