Contrast Enhancement of Lung CT Scan Images using Multi-Level Modified Dualistic Sub-Image Histogram Equalization
K. Ezhilraja, P. Shanmugavadivu · 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS) · 2022
Image enhancement is one of the most important phases of automated in lung cancer detection. Lung cancer is a worldwide fatal disease that claims victims among both men and women. Computed Tomography (CT) is the most effective predominantly used medical imaging modality for diagnosing uncontrolled lung cell growth. The detection of lung cancer from a CT scan using visual perception requires extensive visual abilities and expertise. The proposed Multi-Level Modified Dualistic Sub Image Histogram Equalization (ML-DSIHE) separates the histogram of a CT image into two parts using the mid-point of the intensity scale. Then ML-DSIHE iteratively divides the bifurcated histogram based on the median and then histogram equalization independently on those partitioned histogram. The proposed ML-DSIHE offers a solution for over-enhancement and under-enhancement of background, loss of foreground Gray-Level components, and noise amplification. The M-DSIHE is confirmed to be a novel enhancement method and thus greater aids in the early detection of lung cancer improved accuracy. The ML-DSIHE is found to the accuracy of early detection of lung cancer. Moreover, this enhancement technique is computationally simple and thus takes less computational time.