Prostate Cancer Classification Technique on Pelvis CT Images

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Prostate cancer is one of the commonest cancer found: the forth in Malaysia and sixth in the planet with 307 000 mortalities in 2012. Early detection is important to reduce the death rate; thus this research is carried out to develop an automated prostate cancer classification from the bone scan of pelvis CT images. Preliminary experiment has been carried out using real images from Hospital Chancellor Tuanku Muhriz (formerly known as UKM Medical Center) database. The detection algorithm was developed and compared between Random Forest and Logistic Regression classification of normal and abnormal cancerous prostate image. Furthermore, random tree was selected as the mining technique to model the knowledge in radiology department in predicting the level of prostate cancer severity. A total of 51 cases of prostate cancer patients with 2 urologists, a radiologist and an expert on pathology of PPUKM actively involved in the development of the expert system. The classification has achieved 90% accuracy percentage with 10-fold cross validation technique with Logistic Regression. In addition, the expert system grading on test set has achieved 96% accuracy with the same learning technique.



  • Keywords

    bone scan classification , cancer detection, cancer expert system,.

  • References

      [1] Zainal Ariffin Omar & Tamin, N. S. I., National Cancer Registry Report 2007, 2011.

      [2] Fairul Asmaini Mohd Pilus, “Awas kanser prostat”, Newspaper of Harian Metro, 2015.

      [3] Denis Campbell, “Almost half of cancer patients diagnosed too late”, Newspaper of The Guardian, 2014.

      [4] CancerCare, “Prostate Cancer: Fourth Most Common Cancer Among Malaysian Men”, AXA Affin, 2016.

      [5] M.J.P. Castanho, F. Hernandes, A.M. De Ré, S. Rautenberg & Billis, A, “Fuzzy expert system for predicting pathological stage of prostate cancer”, Journal of Expert Systems with Applications, Vol. 40, No. 2, pp. 466-470, 2013.

      [6] Murat Cinar, Mehmet Engin, Erkan Zeki Engin & Ateşçi, Y. Z., “Early prostate cancer diagnosis by using artificial neural networks and support vector machines”, Journal of Expert Systems with Applications, Vol. 36, No. 3, pp.6357-6361, 2009.

      [7] G. Lemaître, R. Martí, J. Freixenet, J. C. Vilanova, P. M. Walker, and F. Meriaudeau, “Computer Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review”, Journal of Computer in Biology and Medicine, Vol. 60, pp. 8-31, 2015.

      [8] Adam A., Bulpitt A. and Treanor D., “Grading Dysplasia in Barrett’s Oesophagus Virtual Pathology Slides with Cluster Co-occurrence Matrices”, In Proc. of Histopathology Image Analysis: Image Computing in Digital Pathology in conjunction with The 15th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2012.

      [9] El-Dahshan, E., Salem, A. B. M. and Younis, T. H., “A Hybrid Technique for Automatic MRI Brain Images Classification”, Studia Univ. Babes-Bolyai, Informatica, Vol. 54, No.1, pp. 55-67, 2009.

      [10] Yazan M. Alomari, Siti Norul Huda Sheikh Abdullah, Reena Rahayu Md Zin, Khairuddin Omar, “Iterative randomized irregular circular algorithm for proliferation rate estimation in brain tumor Ki-67 histology images”, Journal of Expert Systems with Applications, Vol. 48, pp.111-129, 2016.

      [11] Dheeb Albashish,Shahnorbanun Sahran, Azizi Abdullah,Afzan Adam, Nordashima Abd Shukor,Suria Hayati Md Pauzi, “Multi-scoring feature selection method based on SVM-RFE for prostate cancer diagnosis”, The 5th International Conference on Electrical Engineering and Informatics 2015 , pp. 682-686, 2015.

      [12] Alomoush, WK., Abdullah, S. N. H. S., Sahran, S. & Hussain, R. I., “Segmentation of MRI brain images using FCM improved by firefly algorithms”, Journal of Applied Sciences, Vol. 14, pp. 66-71, 2014.

      [13] Kaidar, S. M., Hussain, R. I., Bohani, F. A., Sahran, S., binti Zainuddin, N., Ismail, F., Thanabalan, J., Kalimuthu, G. & Abdullah, S. N. H. S., “Brain tumor treatment advisory system”, Journal of Soft Computing Applications and Intelligent Systems, pp. 78-88, 2013.

      [14] W. N. A. Baharuddin,S. N. H. S. Abdullah, S. Sahran, A. Qasem, A. bin Abdullah R. Iqbal, F. Ismail, “Type 2 Fuzzy Logic for mammogram breast tissue classification”, International Conference on Industrial Informatics and Computer Systems (CIICS), pp. 1-6, 2016.

      [15] Siti Norul Huda Sheikh Abdullah, Farah Aqilah Bohani, Baher H. Nayef, Shahnorbanun Sahran, Omar Al Akash, Rizuana Iqbal Hussain, and Fuad Ismail, “Round Randomized Learning Vector Quantization for Brain Tumor Imaging”, Journal of Computational and Mathematical Methods in Medicine, Vol. 2016, p. 19, 2016.

      [16] Ponraj, D. N., Jenifer, M. E., Poongodi, P. and Manoharan, J. S., “Morphological Operations for the Mammogram Image to Increase the Contrast for the Efficient Detection of Breast Cancer”, European Journal of Scientific Research, Vol. 68, No. 4, pp. 494-505, 2012.

      [17] Shaoqing, Z. and Lu, X., “The Comparative Study of Three Methods of Remote Sensing Image Change Detection”, Proceedings of ISPRS Congress, Istanbul, Turkey, pp.12-23, 2008.

      [18] Wilson, G. X. R. J. N., Handbook of Computer Vision Algorithms in Image Algebra CRC Press, CRC Press LLC, 1996.

      [19] Patnaik, C. S. P. a. S., “Filtering Corrupted Image and Edge Detection in Restored Grayscale Image Using Derivative Filters”, International Journal of Image Processing 3, pp. 105-119, 2009.

      [20] Gupta, G., “Algorithm for Image Processing Using Improved Median Filter and Comparison of Mean, Median and Improved Median Filter”, International Journal of Soft Computing, Vol. 1, No. 5, pp. 304-311, 2011.

      [21] Punamthakare, “A Study of Image Segmentation and EdgeDetection Techniques”, International Journal on Computer Science and Engineering, Vol. 3, No.2, pp. 6, 2011.

      [22] Eleyan, A. and Demirel, H., “Co-Occurrence Matrix and Its Statistical Features as a New Approach for Face Recognition”, Turkish Journal of Electrical Engineering and Computer Sciences, Vol. 19, No. 1, pp. 97-107, 2011.

      [23] Dougherty, E.R. dan LotufoR.A., “Hands-on morphological image processing”, SPIE-International Society for Optical Engineering, Washington, USA, 2003.

      [24] El-Dahshan, E., Salem, A. B. M. and Younis, T. H., “A Hybrid Technique for Automatic MRI Brain Images Classification”, Studia Univ. Babes-Bolyai, Informatica, Vol. 54, No.1, pp. 55-67, 2009.

      [25] Breiman, L., Random forest, Kluwer Academic Publishers, pp. 5-32, 2001.




Article ID: 24904
DOI: 10.14419/ijet.v8i1.2.24904

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.