An Imaging Based Lung Tissue and Spread Level Detection for Early Detection of Cancer
B. Thiyaneswaran1, K. Anguraj2, R. Kandiban3, N. S. Yoganathan4, J. Jayanthi5 

1B. Thiyaneswaran, Department of Electronics & Communication Engineering, Sona College of Technology, Salem, (Tamil Nadu), India.
2K. Anguraj, Department of Electronics & Communication Engineering, Sona College of Technology, Salem, (Tamil Nadu), India.
3R. Kandiban, Cables and Diagnostics Division, Central Power Research Institute, Bangalore, Karnataka, India.
4N. S. Yoganthan, Department of Electronics & Communication Engineering Sona College of Technology, Salem, (Tamil Nadu), India.
5J. Jayanthi, Department of Computer Science, Sona College of Technology, Salem, (Tamil Nadu), India.

Manuscript received on 14 March 2019 | Revised Manuscript received on 18 March 2019 | Manuscript published on 30 July 2019 | PP: 3381-3387 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3168078219/19©BEIESP | DOI: 10.35940/ijrte.B3168.078219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: A tissues in the human organs is mainly due to the disease. The spread of tissue indicates the increase in disease level. The tissues and level of tissue in the organ may require for physician assessment. The proposed work is used to detect the tissue level in lungs. An early detection of tissue may leads to detect the cancer cell. The RGB color input image is converted into gray scale image. The gaussian noise is applied on the gray image. The median filter is applied to project the tissue pixels. The canny edge detection is applied on the filtered image to detect the boundary regions. The gradient magnitude operation is performed to project the edges of tissue. A watershed transform is applied on the gradient image to perform the morphological operation. A morphological area open and area close operation is performed along with reconstruction which highlights the tissue area from the lungs. Super imposing morphology with the regional maximum pixel operation is performed to differentiate tissue pixels. The tissue area is retained and all other area pixels are replaced by logic ‘0’ pixels using Otsu’s global thresholding method. The tissue portion is cropped using the OAD regions.
Keywords: Lung Tissue, Median Filter, Watershed, Morphological, Cropping.

Scope of the Article: Bio – Science and Bio – Technology