Medical image registration with object deviation estimation through motion vectors using octave and level sampling

• Published in: Automatika Vol. 65; no. 3; pp. 1213 - 1227
• Main Authors: Nagarathna, P., Jeelani, Azra, Fiza, Samreen, Vasu, G. Tirumala, Seelam, Koteswararao
• Format: Journal Article
• Language: English
• Published: Ljubljana Taylor & Francis Ltd 02-07-2024; Taylor & Francis Group
• Subjects: Determinants; Deviation; differential operators; feature point detection; feature point selection; Hessian matrices; Image analysis; Image registration; Lower bounds; Medical image registration; Medical imaging; motion estimation; Motion perception; motion vectors; Object recognition; Registration; Sampling
• ISSN: 0005-1144; 1848-3380
• DOI: 10.1080/00051144.2024.2353543

Medical image analysis presents a significant problem in the field of image registration. Recently, medical image registration has been recognized as a helpful tool for medical professionals. Current state-of-the-art approaches solely focus on source image registration and lack quantitative measurement for object deviation in terms of loosening, subsidence and anteversion related to surgery. In this article, we have provided motion vectors for recognizing the object deviation, in addition to detecting and selecting the feature points. Firstly, the feature points will be detected using Hessian matrix determinants and octave and level sampling. Then the strongest feature points are selected which will be utilized for identifying the object deviation with respect to the reference image through motion vectors. The objective of this work is to combine image registration and temporal differencing to achieve independent motion detection. In comparison to state-of-the-art approaches, the proposed methodology achieves higher Information Ratio (IR), Mutual Information Ratio (MIR) and their lower bounds for image registration.