Automatic Segmentation of Lung Tumor from X-Ray Images Using Advance Novel Semantic Approach

Abstract

In this modern era, the clinical laboratory has become more interested in obtaining correct test results, particularly when diagnosing pulmonary tumors. Lung tumors are critical for the diagnosis and treatment at different stages, including benign, malignant. This document focuses on the pulmonary segmentation function. The area of the tumor in the lung is measured by three steps. The first step is to acquire an image that reads and re-dimensions the pulmonary image. The second stage consists of the processing of the image using the median filter with histogram equalization (MFHE) technique. The third stage consist the segmentation process such as, the fuzzy c-means, Otsu and the region-based active contours toward the advanced semantic segmentation methodology indicated is checked. The results suggest that the overall estimate of the proposed methodology is more reliable. The segmentation results indicate that the proposed calculation improves the distinguishing of the tumor area. The performance indicators such as PSNR, MSE, SSIM and RMSE, Dice are five general to be calculated. The programming for MATLAB 2018 has been used to validate the algorithm for the effective tumor area evaluation. This proposed segmentation algorithm for radiographic images is the best automatic segmentation algorithm for tumors based on segmentation assessments. The segmentation results demonstrate that the proposed estimate improves the accurate identification of the tumor area.