Journal
IET IMAGE PROCESSING
Volume 12, Issue 3, Pages 337-344Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-ipr.2017.0526
Keywords
image registration; medical image processing; feature extraction; affine transforms; biomedical MRI; computerised tomography; biomedical ultrasonics; ultrasonic imaging; image coding; Laplace transforms; image texture; image denoising; computational complexity; registration quality; squared intensity error; local binary patterns; local tetra patterns; local diagonal extrema patterns; minimum registration error; computational complexity; floating images; moving images; noisy versions; target-fixed images; improved Procrustes analysis-based affine transformation; MRI; magnetic resonance imaging; computed tomography; LDLP feature histograms; ultrasound images; local texture analysis; centre-diagonal pixel correlation; primitive idea; binary feature vector; encoding; diagonal neighbours; LDLP method; low-dimensional binary feature descriptor local diagonal Laplacian pattern; swift clinical diagnosis; extracted features; image analysis; pattern recognition; modified affine transform; medical image rigid registration
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Robust and reliable features with noise immunity, rotation-invariance, and low-dimensionality are the challenging aspects of pattern recognition. In this study, the authors presented a novel low-dimensional binary feature descriptor local diagonal Laplacian pattern (LDLP) for medical image registration. LDLP method is developed by defining the local relationship between a centre pixel and its diagonal neighbours and encoding it to a binary feature vector. The idea of centre-diagonal pixel correlation has drastically reduced the length of the feature vector without compromising the quality of local texture analysis. In the proposed work, first, the LDLP feature histograms of computed tomography (CT), magnetic resonance (MR), and ultrasound images are obtained. Further, these LDLP features of individual medical images are considered as target/fixed objects while their corresponding rotated and noisy features are considered as moving/floating objects to perform mono-modal rigid registration using an improved Procrustes analysis-based affine transform. The registration quality is examined by calculating the squared intensity error and the results are compared with the existing binary patterns such as local binary patterns, local tetra patterns, and local diagonal extrema patterns. The proposed LDLP feature descriptor-based rigid registration has attained relatively better performance in terms of registration accuracy and computational complexity.
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