Brain Tumor Diagnosis Based on Lasso Classifier

Brain Tumor Diagnosis Based on Lasso Classifier

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)
© 2014 by IJETT Journal
Volume-15 Number-8
Year of Publication : 2014
Authors : K.Lakshmi Narayana , M.Mariya Dasu

Citation 

K.Lakshmi Narayana , M.Mariya Dasu. "Brain Tumor Diagnosis Based on Lasso Classifier", International Journal of Engineering Trends and Technology (IJETT), V15(8),374-378 Sep 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract

Computed tomography images are widely used in the diagnosis of brain tumour because of its faster processing, avoiding malfunctions and suitability with physician and radiologist. This study proposes a new approach to automated detection of brain tumour. This proposed work consists of various stages in their diagnosis processing such as reprocessing, anisotropic diffusion, feature extraction and classification. The local binary patterns and gray level co-occurrence features, gray level and wavelet features are extracted and these features are trained and classified using LASSO classifier. The achieved results and quantitatively evaluated and compared with various ground truth images. The proposed method gives fast and better segmentation and classification accuracy. We use free-response receiver operating characteristic curves to evaluate detection performance, and compare the proposed algorithm with several existing methods. In our experiments, the proposed framework outperformed all the other methods tested.

References

1. El-Naqa, M. N. Wernick, Y. Yang, and N. P. Galatsanos, “Image retrieval based on similarity learning,” in Proc. IEEE Int. Conf. Image Processing, Vancouver, BC, Canada, 2000, pp. 722–725.
2. A. I. Mushlin, R. W. Kouides, and D. E. Shapiro, “Estimating the accuracy of screening mammography: a meta-analysis,” Amer. J. Preventive Med., vol. 14, no. 2, pp. 143–153, 1998.
3. R. N. Strickland and H. L. Hahn, “Wavelet transforms for detecting microcalcifications in mammograms,” IEEE Trans. Med. Imag., vol. 15, no. 2, pp. 218–229, 1996.
4. E. A. Sickles, “Mammographic features of 300 consecutive nonpalpable breast cancers,” Amer. J. Roentgenol., vol. 146, pp. 661–663, 1986.
5. D. B. Kopans, “The positive predictive value of mammography,” Amer.J. Roentgenol., vol. 158, pp. 521–526, 1992.
6. J. G. Elmore, C. K.Wells, C. H. Lee, D. H. Howard, and A. R. Feinstein, “Variability in radiologists’ interpretations of mammograms,” New Engl. J. Med., vol. 331, no. 22, pp. 1493–1499, 1994.
7. R. M. Nishikawa,M. L. Giger,K. Doi, C. J. Vyborny, and R. A. Schmidt, “Computer aided detection of clustered microcalcifications in digital mammograms,” Med. Biological Eng. Computing, vol. 33, pp. 174–178, 1995.
8. P. L. Miller, “Critiquing anesthetic management: the “ATTENDING” computer system,” Anesthesiology, vol. 58, pp. 362–369, 1983.
9. B. Ripley, Pattern Recognition Neural Networks. Cambridge, U.K.: Cambridge Univ. Press, 1996.
10. V. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
11. M. Pontil and A. Verri, “Support vector machines for 3-D object recognition,” IEEE Trans. Pattern Anal. Machine Intell., vol. 20, pp. 637–646, June 1998.
12. V.Wan andW. M. Campbell, “Support vector machines for speaker verification and identification,” in Proc. IEEE Workshop Neural Networks for Signal Processing, Sydney, Australia, Dec. 2000, pp. 775–784.
13. E. Osuna, R. Freund, and F. Girosi, “Training support vector machines: application to face detection,” in Proc. Computer Vision and Pattern Recognition, Puerto Rico, 1997, pp. 130–136.
14. I. El-Naqa, Y. Yang, M. N. Wernick, N. P. Galatsanos, and R. M. Nishikawa, “A support vector machine for approach for detection of microcalcifications,” IEEE Trans. Med. Imag., vol. 21, pp. 1552–1563, Dec. 2002.

Keywords
Brain Tumour, Lasso Classifier, Segmentation, image.