Automatic Lung Cancer Detection using Sobel & Morphological Operations
Akanksha Soni1, Avinash Rai2, Ekta Shivhare3
1Akanksha Soni*, Electronics & Communication, University Institute of Technology, RGPV Bhopal, Madhya Pradesh, India.
2Dr. Avinash Rai, Electronics & Communication, University Institute of Technology, RGPV Bhopal, Madhya Pradesh, India.
3Ekta Shivhare, Electronics & Communication, University Institute of Technology, RGPV Bhopal, Madhya Pradesh, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 2772-2777 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8834088619/2019©BEIESP | DOI: 10.35940/ijeat.F8834.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cancer is the most dangerous disease that may cause death and lung cancer is one of them which is more common among all. There are various imaging techniques through which organs can be scanned for diagnosis. Lung cancer is a disease that may be caused by unrestrained cell growth in lung. Lung cancer is the most common and most dangerous cancer. CT scan can obtain the lung images, but still it has been recognized manually. Manual lung cancer detection is a challenging task because false error rate may lead you to compromise with human’s life. There are lots of researches that has been done in this field but still failed to obtain high precision with minimal error rate. Here the system proposes automatic lung cancer detection using Sobel & Morphological operations that can acquire good precision along with cancer area detection. Sobel is a gradient edge detection technique through which absolute gradient magnitude is computed in the reference of 2D input lung image that is later dilated with morphological operator. The obtained result is liable to attain high precision with less false alarm rate.
Keywords: Lung Cancer Detection, Sobel, Dilation, Gradient Magnitude, Computed Tomography, Morphological Operations