Knowledge vector representation of three-dimensional convex polyhedrons and reconstruction of medical images using knowledge vector

• Published in: Multimedia tools and applications Vol. 82; no. 23; pp. 36449 - 36477
• Main Authors: Rani, Shilpa, Lakhwani, Kamlesh, Kumar, Sandeep
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2023; Springer Nature B.V
• Subjects: Algorithms; Brain; Computer Communication Networks; Computer Science; Computer vision; Data Structures and Information Theory; Datasets; Image reconstruction; Knowledge representation; Magnetic resonance imaging; Medical imaging; Multimedia Information Systems; Pattern recognition; Special Purpose and Application-Based Systems; Track 2: Medical Applications of Multimedia; Reconstruction; Construction; Syntactic grammar; 3D medical images; Knowledge vector
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-023-14894-0

Three-dimensional image construction and reconstruction plays an important role in various applications of the real world in the field of computer vision. In the last three decades, researchers are continually working in this area because construction and reconstruction is an important approach in medical imaging. Reconstruction of the 3D image allows us to find the lesion information of the patients which could offer a new and accurate approach for the diagnosis of the disease and it adds a clinical value. Considering this, a novel approach is proposed for the construction and reconstruction of the image. First, a syntactic pattern recognition-based algorithm is implemented to extract the features from the 2D image. The proposed algorithm takes an input image and extracts the features from the image and these features (Knowledge vector) consist of direction code and length. In addition, a unique and novel algorithm is developed that can rebuild an image using a knowledge vector. Reconstruction allows us to investigate the interior details of 3D images, such as the object’s size, form, and structure. The proposed algorithm performance is assessed on a medical imaging dataset, and the findings are outperformed. Performances of the proposed algorithms are evaluated on Kaggle brain MRI dataset and Medical MRI datasets which is collected from Pentagram research institute, Hyderabad. As per the experimental analysis, the proposed method gives 93.89% of accuracy on Kaggle brain MRI dataset and 97.24% on Medical MRI dataset which is better than exiting state of art methods.