Abstract
For diagnosis of lung tumors, CT scan of lungs is one of the most common imaging modalities. Manually identifying tumors from hundreds of CT image slices for any patient may prove to be a tedious and time consuming task for the radiologists. Therefore, to assist the physicians we propose an automatic lung tumor detection method based on textural features. The lung parenchyma region is segmented as a pre-processing because the tumors reside within the region. This reduces the search space over which we look for the tumors, thereby increasing computational speed. This also reduces the chance of false identification of tumors. For tumor classification, we used GLCM based textural features. A sliding window is used to search over the lung parenchyma region and extract the features. Chi-Square distance measure is used to classify the tumor. The performance of GLCM features for tumor classification is evaluated with the histogram features.
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Hossain, M.R.I., Ahmed, I., Kabir, M.H. (2015). Automatic Lung Tumor Detection Based on GLCM Features. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9010. Springer, Cham. https://doi.org/10.1007/978-3-319-16634-6_9
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DOI: https://doi.org/10.1007/978-3-319-16634-6_9
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