Open AccessDetection of Lung Cancer Stages on Computed Tomography Image Using Laplacian Filter and Marker Controlled Watershed Segmentation Technique
Author(s) -
Tamanna Tajrin,
M. Ahmed,
Sabina Zaman
Publication year - 2022
Publication title -
periodica polytechnica. electrical engineering and computer science
Language(s) - English
Resource type - Journals
eISSN - 2064-5279
pISSN - 2064-5260
DOI - 10.3311/ppee.19755
Subject(s) - stage (stratigraphy) , lung cancer , image processing , artificial intelligence , computer science , image segmentation , segmentation , radiology , cancer , lung , digital image processing , medicine , pattern recognition (psychology) , computer vision , image (mathematics) , pathology , paleontology , biology
Lung cancer is a form of malignant tumor distinguished by aggressive multiplication of abnormal cells in lung tissues. If we can assure the detection of lung cancer in the early stage, then we have a chance to increase the survival rate by five years as effective treatment is still available at this stage. Many researchers in the field of image processing sector have built various systems to detect cancer by using image processing techniques. Internationally TNM (Tumor, Nodule, Metastases respectively) method is followed by a physician and radiologist to describe the stage of lung cancer. Our proposed system uses image processing techniques to detect and classify the tumor according to the TNM staging method. First, a series of image processing techniques are performed in a Computed tomography (CT) image. Then, features are extracted to identify the region of interest (ROI). In our proposed system, the classification approach is different from the reviewed existing systems, and the detection rate is comparatively high.