Reconstruction of Greyscale X-Ray for Boundary Analysis of Anatomical Structure

In medical imaging, accurate boundary detection of anatomical structures is crucial for disease diagnosis and treatment planning. Traditional X-ray imaging, however, generates 2D greyscale images that frequently lack the clarity and resolution required for precise boundary analysis. In order to improve the boundary analysis of anatomical structures, we provide a unique method for reconstructing greyscale X-ray pictures in this study. Our method makes better use of internal structure visualization and boundary identification by reconstructing 3D volumetric data from multiple 2D X-ray images using Deep Learning techniques and N eural Networks. This research paper focuses on the Reconstruction of Greyscale X-ray images to facilitate Boundary Analysis of Anatomical Structures for orthopaedic surgeries. Our study utilizes the X-ray images in DICOM format from the LIDC-IDRI dataset obtained from the Cancer Imaging Archive, employing a series of steps to reconstruct 3D images and detect boundaries within the lung region. Basic pre-processing techniques are applied to the acquired X-ray images to enhance their quality and improve subsequent analysis. Subsequently, segmentation is performed to isolate the bounded lung area from the images. This segmentation process enables focused analysis and accurate boundary detection. Sinograms are generated from the segmented images through the utilization of the Inverse Radon Transform technique. This step captures X-ray attenuation measurements from different angles, providing the foundation for subsequent image reconstruction. The reconstruction part of our project involves the implementation of both the Algebraic Reconstruction Technique and Filtered Back Projection. FBP, a widely used algorithm in CT imaging, is used to reconstruct the 3D image based on the filtered sinogram data. The ART algorithm, known for its iterative nature, refines the image reconstruction through an iterative process that incorporates acquired and synthetic projection data. The reconstructed images obtained through FBP and ART techniques are compared to assess their respective results which aids in evaluating the suitability and performance of each technique for boundary analysis of anatomical structures. Furthermore, the same dataset is utilized to detect the boundaries within the lung region. The reconstructed 3D images are employed for accurate and precise boundary detection, providing crucial information for orthopaedic surgical planning. The outcomes of this study contribute to the field of orthopaedic surgery by providing reconstructed 3D images that facilitate boundary analysis of anatomical structures. The accurate detection of boundaries within the lung region assists medical professionals in surgical planning, aiding in improved surgical precision and patient outcomes. The findings of this research paper offer valuable insights into the application of image reconstruction and boundary analysis techniques in orthopaedic surgery using grayscale X-ray images. We think that the use of our suggested strategy to medical imaging for illness diagnosis and treatment planning may have significant ramifications.