Fuzzy Based Contrast Enhancement Method for Lung Cancer CT Images (International Journal of Engineering and Computer Science, 2017)
Computer aided diagnosis (CAD) has become most commonly used tool in the diagnosis and analysis of medical images such as Lung cancer detection, Brest cancer detection etc. Different type of medical image modalities are used in the diagnosis process by radiologist and physician such as radiographic images ,computer tomography (CT) images, magnetic resonance images (MRI), positron emission tomography (PET) etc. In all analysis if the image quality is not proper then the performance of the CAD system is adversely effected. This work proposed a new Background suppressed fuzzy contrast enhancement (BSFCE) method to improve contrast of the CT images of lung cancer patient. The performance of proposed method have been extensively evaluated and compared with the two existing contrast enhancement techniques Histogram Equalization (HE) and Adaptive Histogram Equalization (AHE) techniques in terms of Mean, Variance and Contrast-per-pixel (CPP).The results reflects that the proposed method outperforms the HE and AHE techniques.