TWO LEVEL DENOISING WITH IMAGE BOUNDARY PIXEL BASED SEGMENTATION OF LUNG MRI FOR ACCURATE LUNG TUMOR DETECTION

Cancer is characterized by abnormal clusters of cells and manifests itself in a variety of ways. In India, lung cancer is the second leading killer as per the records of Indian Cancer Society. Patients have a far better chance of survival when they undergo early detection procedures using Magnetic Resonance Imaging (MRI) scans. The ability to segment MRI scans is crucial for accurate diagnosis and treatment in the clinic, making this an essential and important task. Nevertheless, flaws like low contrast, noise, intensity inhomogeneity, etc., frequently taint MRI images. Clinical diagnostics and medical research rely heavily on MRI, however noise interference frequently compromises the imaging process. This noise comes from all over, and it lowers the image quality, which makes it harder for doctors to make correct diagnoses based on the information they see. Conventional denoising techniques fail to account for MRI images' more intricate forms of noise, such as Rician noise, because they presume that noise is normally distributed normally. Consequently, denoising is still an important and difficult task. Inhomogeneous intensities, juxta-pleural nodules, image noises, and other similar issues make accurate lung segmentation from medical images a continuing problem. An efficient noise-denoising approach for images should keep critical edges intact. There have been several attempts to eliminate noise using image denoising algorithms, however it is often not possible to do so completely. But it takes a long time, is easy to make mistakes with, and needs medical knowledge. This research aims to increase the accuracy of lung area segmentation in MRI images by proposing an updated two-level denoising and segmentation framework for lung region segmentation preserving the accurate lung border. To denoise MRI images of the lungs while keeping their outlines intact, the proposed system initially employs a filtering strategy based on image decomposition. The MRI images of the lungs are subsequently segmented using a combination of enhanced morphological techniques. By utilizing a contour correction strategy that is based on a quick corner detection technique, the segmentations are further enhanced in order to rectify and smooth the retrieved lung outlines. This research proposes a Two Level Denoising with Image Boundary Pixel based Segmentation (TLD-IBPbS) model for accurate lung tumor detection. The proposed model is compared with the traditional models and the results represent that the proposed model performance is high.