CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Medical Image Segmentation for Liver Diseases: A Survey

by Tarek M. Hassan, Mohammed Elmogy, Elsayed Sallam
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 118 - Number 19
Year of Publication: 2015
Authors: Tarek M. Hassan, Mohammed Elmogy, Elsayed Sallam
10.5120/20856-3629

Tarek M. Hassan, Mohammed Elmogy, Elsayed Sallam . Medical Image Segmentation for Liver Diseases: A Survey. International Journal of Computer Applications. 118, 19 ( May 2015), 38-44. DOI=10.5120/20856-3629

@article{ 10.5120/20856-3629,
author = { Tarek M. Hassan, Mohammed Elmogy, Elsayed Sallam },
title = { Medical Image Segmentation for Liver Diseases: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 118 },
number = { 19 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 38-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume118/number19/20856-3629/ },
doi = { 10.5120/20856-3629 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:02:11.629809+05:30
%A Tarek M. Hassan
%A Mohammed Elmogy
%A Elsayed Sallam
%T Medical Image Segmentation for Liver Diseases: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 118
%N 19
%P 38-44
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the recent advances in the field of artificial intelligence and information technology, the improvement in the interpretation of the medical images has contributed significantly to the early diagnosis of different diseases. Medical images are difficult to process because they have various modalities. Therefore, the physicians cannot adequately detect and diagnosis the diseases in traditional ways. There should be Computer-aided detection/diagnosis (CAD) systems that help physicians to understand medical images. CAD systems are processes that give much information that help to understand the medical images and improve the accuracy of detection/diagnosis of various diseases. CAD systems consist of the segmentation of the lesion, extraction of features, and characterization of diseases by means of a classifier. There are many different CAD systems that have been proposed to diagnosis various diseases of various organs of the human body, such as liver and brain. This paper, introduces current different methods of segmentation based on medical images. In addition, this paper also concentrates on the work of different segmentation and classification techniques that have been proposed to diagnosis various liver diseases.

References
  1. Priyanjana Sharma, Shagun Malik, Surbhi Sehgal and Jyotika Pruthi, " Computer Aided Diagnosis Based on Medical Image Processing and Artificial Intelligence Methods", International Journal of Information and Computation Technology. ISSN 0974-2239 Vol. 3, No. 9, pp. 887-892, 2013.
  2. Klaus D. Toennies, "Guide to Medical Image Analysis Methods and Algorithms", Advances in Computer Vision and Pattern Recognition, ISSN 2191-6586, Springer-Verlag London Limited, 2012.
  3. B. Dong, A. Chien, and Z. SHEN, "Frame Based Segmentation for Medical Images," Communications inMathematical Sciences, Vol. 32, No. 4, pp. 1724-1739, 2010.
  4. Jinshan Tang, Sos Agaian, and Ian Thompson, " Guest Editorial: Computer-Aided Detection or Diagnosis (CAD) Systems", IEEE System Journal, Vol. 8, No. 3, September 2014.
  5. Sheenum Marwaha, Himanshu Monga,and Shelza, " Automatic Diagnosis Systems Using Image Processing- A Systematic Study", IRACST - International Journal of Computer Science and Information Technology & Security (IJCSITS), ISSN: 2249-9555 Vol. 2, No. 2, April 2012
  6. Gunasundari S and Janakiraman S, "A Study of Textural Analysis Methods for the Diagnosis of Liver Diseases from Abdominal Computed Tomography", International Journal of Computer Applications, Vol. 74 – No. 11, 2013.
  7. Geert Litjens, Oscar Debats, Jelle Barentsz, Nico Karssemeijer, and Henkjan Huisman, " Computer-Aided Detection of Prostate Cancer in MRI", IEEE Transaction On Medical Imaging, Vol. 33, No. 5, MAY 2014.
  8. M. S. Abirami and Dr. T. Sheela, "Analysis of Image Segmentation Techniques for Medical Images", Proceedings of International Conference on Emerging Research in Computing, Information, Communication and Applications, 2014.
  9. Salem Saleh Al-amri, N. V. Kalyankar, and Khamitkar S. D, " Image Segmentation by Using Threshold Techniques", JOURNAL OF COMPUTING, Vol. 2, Issue 5, ISSN 2151-9617, May 2010.
  10. Er. Pratibha Thakur and Er. Nishi Madaan, "A SURVEY OF IMAGE SEGMENTATION TECHNIQUES", INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS, Vol. 2 Issue. 4, pp. 158-165, April 2014.
  11. P. Elayaraja and M. Suganthi," Survey on Medical Image Segmentation Algorithms", International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 11, November 2014.
  12. K. S. Selvanayaki and R. M. Somasundaram , "A Survey on Image Segmentation Techniques for Edge Detection", International Conference on Innovation in Communication, Information and Computing (ICICIC), 2013.
  13. Rajiv Kumar , Arthanariee A. M,. "A Comparative Study of Image Segmentation Using Edge-Based Approach", International Journal of Mathematical, Computational, Natural and Physical Engineering Vol. 7 No. 3, 2013.
  14. Santanu Bhowmik, Viki Datta, "A Survey on Clustering Based Image Segmentation", International Journal of Advanced Research in Computer Engineering & Technology Vol. 1, Issue 5, ISSN: 2278 – 1323, July 2012.
  15. Ajala Funmilola A, Oke O. A, Adedeji T. O, Alade O. M, Adewusi E. A, "Fuzzy k-c-means Clustering Algorithm for Medical Image Segmentation", Journal of Information Engineering and Applications, Vol 2, No. 6, ISSN 2224-5782 (print) ISSN 2225-0506 (online), 2012.
  16. R. Ravindraiah and K. Tejaswini, " A Survey of Image Segmentation Algorithms Based On Fuzzy Clustering", International Journal of Computer Science and Mobile Computing, Vol. 2, Issue. 7, pg. 200 – 206, July 2013.
  17. Pritesh Vora and Bhavesh Oza, " A Survey on K-mean Clustering and Particle Swarm Optimization", International Journal of Science and Modern Engineering (IJISME) ISSN: 2319-6386, Vol. 1, Issue 3, February 2013.
  18. Deepti Mittal, Vinod Kumar, "Neural network based focal liver lesion diagnosis using ultrasound images Computerized Medical Imaging and Graphics, vol. 35, pp. 315–323, 2011.
  19. Jae Hyun Jeon, Jae Young Choi, Sihyoung Lee, and Yong Man Ro, "Multiple ROI selection based focal liver lesion classification in ultrasound images", Expert Systems with Applications 40, pp. 450-457, 2013.
  20. Andreia Andrade Jose and Silvestre Silva, "Classifier Approaches for Liver Steatosis using Ultrasound Images", Procedia Technology, vol. 5, pp. 763–770, 2012.
  21. Ali A. Sakr, Magdi Elias Fares, and Mai Ramadan, "Automated Focal Liver Lesion Staging Classification based on Haralick Texture Features and Multi-SVM", International Journal of Computer Applications (0975 – 8887) Vol. 91 – No. 8, April 2014.
  22. Nidaa Al-deek, Raja S. Al-Omari, M. B. Al-Zoubi, and Hazem Hiary, "Liver Segmentation from Abdomen CT Images with Bayesian Model" , Journal of Theoretical and Applied Information Technology Vol. 60 No. 3 Feb. 2014.
  23. S. S. Kumar, R. S. Moni, and J. Rajeesh, " Automatic Segmentation of Liver and Tumor for CAD of Liver " JOURNAL OF ADVANCES IN INFORMATION TECHNOLOGY, Vol. 2 No. 1, Feb. 2011.
  24. Wang L, Zhang Z , Liu J, Jiang B, Duan X, Xie Q, Hu D, and Li Z, "Classification of Hepatic Tissues from CT Images Based on Texture Features and Multiclass Support Vector Machines", In: Proc of International Symposium on Neural Networks: Advances in Neural Network, ISNN 2009, Vol. 5552, pp. 374-381, May 2009.
  25. A. Anuja Merlyn and A. Anuba Merlyn, " A Systematic Analysis System for CT Liver Image Classification and Image Segmentation by Local Entropy Method", IJISET - International Journal of Innovative Science, Engineering & Technology, Vol. 1 Issue 3, May 2014.
  26. Jitendra Virmani, Vinod Kumar, Naveen Kalra, and Niranjan Khandelwal, " PCA-SVM-based CAD System for Focal liver lesions using B-mode ultrasound Images", Defence Science Journal, Vol. 63, No. 5, pp. 478-486, Sept. 2013.
  27. Ricardo Ribeiro Rui Marinho, J. V. F. R. & Sanches, J. M. "Chronic Liver Disease Staging Classification Based On Ultrasound, Clinical And Laboratorial Data", IEEE International Symposium, Biomedical Imaging: From Nano to Macro, pp. 707-710, 2011.
  28. Ricardo Ribeiro and Joao Sanched, " Fatty liver characterisation and classification by Ultrasound", IbPRIA 2009, LNCS 5524, Springer-Verlag Berlin Heidelberg, pp. 354-361, 2009.
  29. Ricardo Ribeiro, Rui Tato Marinho, "The Usefulness of Ultrasound in the Classification of Chronic Liver Disease", IEEE Annual International Conference, Engineering in Medicine and Biology Society, EMBC, pp. 5132-5135, 2011.
  30. Ricardo Ribeiro, Rui Marinho, Jose Velosa, Fernando Ramalho, and J. Miguel Sanches. "Diffuse Liver Disease Classification from Ultrasound Surface Characterization, Clinical and Laboratorial Data", Springer-Verlag Berlin Heidelberg, pp. 167–175, 2011.
  31. S. S. Kumar and Dr R. S. Moni, "Diagnosis of Liver Tumor from CT Images Using Fast Discrete Curvelet Transform", IJCA Special Issue on "Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications" CASCT, 2010.
  32. Moti Freiman, O?r Cooper, Dani Lischinski, and Leo Joskowicz, "Liver tumors segmentation from CTA images using voxels classi?cation and af?nity constraint propagation", International Journal of Computer Assisted Radiology and Surgery, Vol. 6, Issue 2, pp. 247-255, March 2011.
  33. R. Rajagopal and P. Subbaiah, "A SURVEY ON LIVER TUMOR DETECTION AND SEGMENTATION METHODS", ARPN Journal of Engineering and Applied Sciences, VOL. 10, NO. 6, APRIL 2015.
Index Terms

Computer Science
Information Sciences

Keywords

Computer Aides Diagnosis (CAD) Systems Medical Image Segmentation Classification Diagnosis of Liver Diseases.

Powered by PhDFocusTM