Identification of Efficient Patterns for the Detection of
Lung Cancer
Journal:
GRENZE International Journal of Computer Theory and Engineering
Authors:
K Guna Shekar, Pancham Bopanna D, C Jayvardhan Chetty, Basavaraj V Hiremath, S C Prasannakumar
Volume:
3
Issue:
4
Grenze ID:
01.GIJCTE.3.4.79
Pages:
533-537
Abstract
Lung cancer is one of the most occurring cancers among both men and women
[1]. Detection of cancer at the early stage is the only method to improve the survival rate.
CT images of lung are helpful in the diagnosis of lung cancer. Doctor analyses the CT image
and predicts the presence of cancer nodule. This manual detection can also result in
false detection. So in order to overcome this problem a computerized method for cancer
detection is needed. Image processing technique can be used for this purpose. By using the
following image processing techniques lung cancer detection can be developed further. Lung
cancer detection system has three steps to detect the presence of cancer nodule in lung. Preprocessing
stage, segmentation and feature extraction stage. Pre-processing step includes
image enhancement using Gaussian filter and image segmentation. Enhanced CT image of
lung is then fed to segmentation phase. From the segmented output, features are extracted.
By using these extracted features the lung is differentiated as normal lung or cancerous
lung. And further the cancerous tumor can be differentiated as Benign and Malignant.