Performance Evaluation of Histogram Equalization based Enhancement on Lung CT Scan Images
K. Ezhilraja, P. Shanmugavadivu · 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) · 2022
Lung cancer also referred as lung carcinoma is caused due to smoking. The recent report released during World Cancer Day (2022), states that the incidents of lung cancer is radically increasing year by year. It is estimated that India will record about one lakh victims of lung cancer in the next five years. As per the statistics, the number of women affected by lung cancer has also increased over the last decade. Computed Tomography (CT) is the most effective medical imaging techniques used to visualize the location of tumors in lungs for lung cancer detection. However accurate localization of lung cancer tumors still remains as a challenge due to the excessive brightness of CT Scan image. Image enhancement as a pre-processing step plays a major role in the subsequent steps of lung cancer detection from CT scan lung images. This article, reports performance of four select image enhancement methods namely Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Brightness Preserving Bi-Histogram Equalization (BBHE) and Dualistic Sub-Image Histogram Equalization (DSIHE) based on the inferences of quantitative and qualitative metrics. The suitability of these enhancement methods for lung CT scan image is analyzed. It is apparently confirmed that incorporation of suitable image enhancement techniques is a CAD system of lung cancer detection, shall help in the detection of lung cancer with greater accuracy. It is also concluded that the enhanced lung CT scan image facilitate early detection of lung cancer in CAD systems, which is a boon in saving the lives of the victims.