An Improved Image Processing Analysis for the Detection of Lung Cancer using Gabor Filters and Watershed Segmentation Technique

The global increase in population has simultaneously raised the awareness to maintain good health in most of the people. The poor quality of food taken and environmental pollution leads to occurrence of lung cancer in most of the people. It is highly important to detect the lung cancer in earlier stages with minimum time delay and provide a better solution to reduce the lung cancer. Early detection of lung cancer is also desirable for efficient analysis and it helps ophthalmologist to provide the treatment in early stages. Earlier researchers employed methods like Fast Fourier Transform (FFT) for image enhancement, thresholding approach for segmentation and Binarization approach for Extraction etc.,. Research work aiming at computerizing these selections, passing the available lung cancer images and its database in basic three stages like enhancement, segmentation and feature Extraction stage to achieve more quality and accuracy in detection of lung cancer. Approaches developed by the earlier researchers fail to produce accuracy in real time applications. Hence, to overcome the drawbacks of these approaches a new method to detect lung cancer using Gabor filters and watershed segmentation techniques is proposed in this work. The CT (Computed Tomography) images captured from lung cancer patients are analyzed by developing Digital Image processing technique. The results obtained are comparable with standard values obtained from the hospital for real time analysis. Hence, this new technique with Gabor filters and watershed segmentation approach can be used for quick detection of lung cancer. This approach is beneficial to the Medical Equipment's manufacturing industries and also helps the medical practitioners for early detection of lung cancer.