Date of Publication :9th August 2017
Abstract: Lung cancer is the foremost reason of deaths due to cancer disease around the world. As per the report of the World Health Organization (WHO) near about 10 million patients in the world will be deceased because of lung cancer by 2030. Timely inhibition of lung tumor plays an imperative role for survival assistance enhancements. By following the notion that heavy investigation of radiographic imageries can apprise and enumerate the microenvironment and the degree of tumor level heterogeneity for personalized medicine, examination of huge numbers of image features extracted from computed tomography (CT). The focus is on high throughput that can apprehend spatial and temporal genetic heterogeneity in a without operating the patient, which preferred over intrinsic biopsy based molecular assays method. The lung cancer detection is valuable for ongoing medical research and computer-assisted diagnosis of lung cancer. In this paper, we have presented lung cancer detection algorithm that yields possible location of tumor in the lung. The algorithmic steps comprises of histogram equalization of the CT scan image followed by log- Gabor filter bank processing to enhance the CT scan image. Subsequently the image is dilated using gradient mask and after border clearing, the location of possible tumor is detected. This algorithm gives accurate results on publically shared CT scan images
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