R.M. Haralick, K. Shanmugam, and I. Dinstein, Textural Features for. Image Classification, IEEE Trans. on Systems, Man and Cybernetics,. Vol. SMC-3, pp.
Improved segmentation algorithm for brain tumor detection Authors Soumen Biswas Dr. Dibyendu Ghoshal Ranjay Hazra Tushar Singh ICEMS 2016
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Introduction • Automated brain tumor detection can reduce the human error and also reduce the complexity of the manual detection. • It turns to be easier for physicians too if they have an access to arranged standardized information of the patient along with pattern display facilities. • Hence we aim to have logistic automation solution which removes “human touch” thus enhancing operational efficiency and patients safety, where work flow is executed automatically upon receipt of medical images ICEMS 2016
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What is Brain Tumor..?? • Abnormal growth of cells in brain is called brain tumor. • Tumors may be Benign or Malignant
• A Malignant tumor, also called brain cancer, grows rapidly and often invades or crowds healthy areas of the brain. Benign brain tumors do not contain cancer cells and are usually slow growing.
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Proposed Approach • Applying the Gabor filter and Thresholding technique on gradient image. • Calculating the iteration based on threshold value to find out the region of tumor. • Separate the object from the image by applying intensity distribution of morphological operation. • Boundary detection based on the morphological intensity level. • SVM classifier based on the Thresholding region is used to extract the tumor. ICEMS 2016
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Block Diagram of the proposed method
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Simulation Results Analysis… • Quality of the Segmented tumor region is calculated subjectively as well as numerically. • GLCM (Gray Level Co-occurrence matrix) parameters are considered. • Proposed improved method is compared with Thresholding segmentation technique and Watershed segmentation technique. • Numerically and visually the quality of segmented region is better using the proposed method. ICEMS 2016
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Simulation Results Analysis…
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1. Input
MRI Image, 2. Segmented region using thresholding technique, 3. Segmented region using Watershed segmentation, 4. Segmentation region using proposed algorithm
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Simulation Results Analysis…
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Conclusion • Higher values of contrast and energy as well as lower values of Homogeneity and INN (Inverse difference normalized) illustrates the better quality of segmentation region using the improved algorithm. • Simulation results of the proposed method provides false edge free detection of tumor region. ICEMS 2016
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Future Work • Increasing the number of tumor images to evaluate the accuracy results of the improved method of brain tumor detection.
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References 1. M. B. Ahmad, T.-S. Choi, Local threshold and boolean function based edge detection, IEEE Transactions on Consumer Electronics, 45 (3) (1999) 674–679. 2. P. Dhage, M. Phegade, S. Shah, Watershed segmentation brain tumor detection, in: IEEE 2015 International Conference on Pervasive Computing (ICPC), 2015, pp. 1–5. 3. S. Hojjatoleslami, J. Kittler, Region growing: a new approach, IEEE Transactions on Image processing 7 (7) (1998) 1079–1084 4. J.-P. Gambotto, A new approach to combining region growing and edge detection, Pattern Recognition Letters 14 (11) (1993) 869–875 ICEMS 2016
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References 5. Jan Luts, ArendHeerschap, Johan A.K. Suykens, Sabine Van
Huffe, A combined MRI and MRSI based multiclass system for brain tumour recognition using LS-SVMs with class probabilities and feature selection, Artificial Intelligence in Medicine 40 (11) (2007) 87-102 6. R.M. Haralick, K. Shanmugam, and I. Dinstein, Textural Features for Image Classification, IEEE Trans. on Systems, Man and Cybernetics, Vol. SMC-3, pp. 610-621, 1973. 7. Kumar, A., Pang, G.K.H., Defect detection in textured materials using Gabor filters, IEEE Trans. on Industry Application, Vol. 38 (2) (2002) 425-440 8. R. C. Gonzalez, Digital image processing, Pearson Education India, 2009 9. Beucher, Serge, and Fernand Meyer. "The morphological approach to segmentation: the watershed transformation." OPTICAL ENGINEERING-NEW YORK-MARCEL DEKKER INCORPORATED34 (1992): 433-433. ICEMS 2016 12
Thank You
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