Segmentation coupled textural feature classification for lung tumor prediction

• Published in: 2010 IEEE International Conference on Communication Control and Computing Technologies pp. 518 - 524
• Main Author: Anand, S K Vijai
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2010
• Subjects: Back propagation network; Gray level co-occurrence matrix; Histograms; Image segmentation; Lung tumor prediction; Nonlinear total variation denoising; Optimal thresholding; Region growing; Textural features; Training
• ISBN: 9781424477692; 1424477697
• DOI: 10.1109/ICCCCT.2010.5670607

A pulmonary nodule is the most common sign of lung cancer. The proposed system efficiently predicts lung tumor from Computed Tomography (CT) images through image processing techniques coupled with neural network classification as either benign or malignant. The lung CT image is denoised using non-linear total variation algorithm to remove random noise prevalent in CT images. Optimal thresholding is applied to the denoised image to segregate lung regions from surrounding anatomy. Lung nodules, approximately spherical regions of relatively high density found within the lung regions are segmented using region growing method. Textural and geometric features extracted from the lung nodules using gray level co-occurrence matrix (GLCM) is fed as input to a back propagation neural network that classifies lung tumor as cancerous or non-cancerous. The proposed system implemented on MATLAB takes less than 3 minutes of processing time and has yielded promising results that would supplement in the diagnosis of lung cancer.