Efficient Image Processing Based Liver Cancer Detection Method

Yogita Ashok Deore, Prof. Namrata D. Ghuse

Abstract


The liver is the extensive internal organ in the human body. The liver is the second organ most generic involved by metastatic disease being liver cancer one of the prominent causes of death worldwide. without healthy liver a person cannot survive. It is life threatening disease which is very challenging perceptible for both medical and engineering technologists. Med-ical image processing is used as a non-invasive method to detect tumours. The chances of survival having liver Tumour highly depends on early detection of Tumour and then classification as cancerous and non-cancerous tumours. Image processing techniques for automatic detection of brain are includes pre-processing and enhancement, image segmentation, classification and volume calculation, Poly techniques have been developed for the detection of liver Tumour and different liver tumour detection algorithms and methodologies utilized for Tumour diagnosis. Novel methodology for the detection and diagnosis of liver Tumour.

Full Text:

PDF

References


Anisha P R, Kishor Kumar Reddy C, Narasimha Prasad L V A Pragmatic approach for Detecting Liver Cancer using Image Processing and Data Mining Techniques, SPACES-2015, Dept. of ECE, K L UNIVERSITY 2015.

M V Sudhamani, G T Raju,Segmentation and Classification of Tumor in Computed Tomography Liver Images for Detection, Analysis and Preop-erative Planning, International Journal of Advanced Computer Research (ISSN (print): 2249-7277 ISSN (online): 2277-7970) Volume-4 Number-1 Issue-14 March-2014.

L. Ali, A. Hussain, J. Li, U. Zakir, X. Yan, A. Shah, U. Sudhakar, B. Luo Intelligent Image Processing Techniques for Cancer Progression Detection, Recognition and Prediction in the Human Liver, 978-1-4799-5375-2/14 2014 IEEE.

Yu Masuda, Amir Hossein Foruzan, Tomoko Tateyama and Yen Wei Chen., Automatic Liver Tumor Detection Using EM/MPM Algorithm and Shape Information, 13th International Conference on Control, Automa-tion, Robotics & Vision Marina Bay Sands,Singapore, 10-12th December 2014 (ICARCV 2014).

ElMasry and et al., Automatic liver CT image clustering based on invasive weed optimization algorithm, Engineering and Technology, 1-5, IEEE 2014.

AbdallaZidan, Level Set based CT liver image segmentation with water-shed and artificial neural networks, International conference on hybrid intelligence systems, 96-102, IEEE 2012.

Pedro Rodrigues, Jaime Fonseca, Joo L. Vilaa, An Image Processing Application for Liver Tumour Segmentation, 1st Portuguese Meeting in Bioengineering, February 2011

Sangman Kim, Seungpyo Jung, Youngju Park, Jihoon Lee and Jusung Park Effective Liver Cancer Diagnosis Method based on Machine Learn-ing Algorithm, 7th International Conference on Bio-Medical Engineering and Informatics (BMEI 2014)

Nader H, Mohiy M, Khalid M, Fully automatic liver tumour segmentation from abdominal CT scans IEEE 2010.

Vinita Dixit, JyotikaPruthi, Review of Image Processing Techniques for Automatic Detection of Tumor in Human Liver, IJCSMC, Vol. 3, Issue. 3, March 2014, pg.371 378.[11] Dipak Kumar Kole, Saptarshi Bhat-tacharya, Sreeja Mala, Sampita Mandal, Atreyee Sinha, Souptik Sinha, DibyaMukhopadhyay and Aruna Chakraborty, Automatic Detection and Size Measurement of Hepatic Lesions , International Journal of Wisdom BasedComputing, Vol. 1(3), December 2011.

Sajith A. G, Hariharan. S, Medical Image Segmentation Using CT Scans-A Level Set Approach, International Journal of Innovative Tech-nology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-2, Issue-6, May 2013.

Rajesh Dikshit, Prakash C Gupta, Cancer mortality in India: a nationally representative survey Published Online in The Lancet, vol. 379, pp. 1807 -1816, May 2012

S.Priyadarsini and Dr.D.Selvathi, Survey on Segmentation of Liver from CT Image IEEE international conference on Advance communication control and computing technologies, pp. 234-238, August 2012.

Pavlidis, T. Image Analysis. Annual review of Computer Science, 3, pp. 121-146, 1988

Pal, N. R. and Pal, S. K. A review on image segmentation techniques Pattern Recognition, 26(9), pp. 1274-1294, 1993.

Marius George Linguraru, Tumor burden analysis on computed tomog-raphy by Automated Liver and Tumor Segmentation, at IEEE transactions on medical imaging, vol. 31, pp. 1965-1976, October 2012 [10].

Jia Xin-Wang, Ting Ting-Zhang CT Image Segmentation by using a FHNN Algorithm Based on Genetic Approach 3rd Bioinformatics and Biomedical Engineering , ICBBE,pp. 1-4, June 2009.

Seo, K.S. Improved fully Automatic Liver Segmentation using His-togram tail threshold Algorithms Computational Science ICCS, vol. 35, pp. 822-825, May 2005.

M.UsmanAkram, AasiaKhanum and Khalid Iqbal, ”An automated Sys-tem for Liver CT Enhancement and Segmentation”, ICGSTGVIP Journal, vol. 10, pp. 5-10, October 2010.

S.S. Kumar, R.S. Moni, ”Diagonosis of liver tumor from CT images us-ing Fast Discrete Curvelet Transform”, International Journal of Computer Applications, pp. 1-6, July 2010.

Ramanjot Kaur, Lakhwinder Kaur and Savita Gupta, ”Enhanced KMean Clustering Algorithm for Liver Image Segmentation to Extract Cyst Region”, International Journal of Computer Applications, pp. 59-66, July 2011.

K. K. Singh, A. Singh, A Study of Image Segmentation Algorithms for Different Types of Images, International Journal of Computer Science Issues, vol. 7, pp. 1-4, September 2010.

Liu Jian-hua et al, Contour correction liver cancer CT image segmenta-tion method based on Snake model, at 2nd Image and Signal Processing conference, pp. 1-4, October 2009.

Sahar Yousefi, Reza Azmi in their paper comparison and evaluation of three optimization algorithm in MRF model for brain tumor segmentation in MRI.

K.Mala, Neural Network based Texture Analysis of Liver Tumor from Computed Tomography Images, at International Journal of Biological and Life Sciences, vol. 2, pp. 33-40, May 2006.[20] StefanWiemer, Earthquake Statistics and Earthquake Prediction Research, Institute of Geophysics,ETHHnggerberg, Zrich, Switzerland, 2003, pp.1-11.

Hiroo Kanamori, Earthquake Prediction: An Overview, International Handbook of Earthquake and Engineering Seismology, 2003, pp.1205-1216.

Toshi Asda, Earthquake Prediction Study in Japan, Proceedings of Ninth World Conference on Earthquake Engineering, 1988, pp.13-19.

C. G. Sammis and D. Sornette, Positive feedback, memory and the predictability of earthquakes, 2002, pp 25012508.

PiotrPorwik and Agnieszka Lisowska, The HaarWavelet Transform in Digital Image Processing:Its Status and Achievements, Machine Graphics and Vision, 2004, pp.79-98.

Dr.MichealSek, Frequency Analysis Fast Fourier Transform, Frequency Spectrum,


Refbacks

  • There are currently no refbacks.


Copyright © IJETT, International Journal on Emerging Trends in Technology