An Automatic Fuzzy-based Multi-temporal Brain Digital Subtraction Angiography Image Fusion Algorithm Using Curvelet Transform and Content Selection Strategy

• Published in: Journal of medical systems Vol. 38; no. 8; pp. 70 - 16
• Main Authors: Momeni, Saba, Pourghassem, Hossein
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.08.2014; Springer Nature B.V
• Subjects: Algorithms; Angiography; Angiography, Digital Subtraction - methods; Brain; Brain - diagnostic imaging; Coefficients; Digital; Fuzzy Logic; Health Informatics; Health Sciences; Humans; Image Interpretation, Computer-Assisted - methods; Image processing; Image processing systems; Medical imaging; Medicine; Medicine & Public Health; Statistics for Life Sciences; Strategy; Subtraction; Systems-Level Quality Improvement; Wavelet Analysis; Digital subtraction angiography; Fuzzy logic; Multi-temporal image fusion; Curvelet transform; Content selection strategy
• ISSN: 0148-5598; 1573-689X; 1573-689X
• DOI: 10.1007/s10916-014-0070-9

Recently image fusion has prominent role in medical image processing and is useful to diagnose and treat many diseases. Digital subtraction angiography is one of the most applicable imaging to diagnose brain vascular diseases and radiosurgery of brain. This paper proposes an automatic fuzzy-based multi-temporal fusion algorithm for 2-D digital subtraction angiography images. In this algorithm, for blood vessel map extraction, the valuable frames of brain angiography video are automatically determined to form the digital subtraction angiography images based on a novel definition of vessel dispersion generated by injected contrast material. Our proposed fusion scheme contains different fusion methods for high and low frequency contents based on the coefficient characteristic of wrapping second generation of curvelet transform and a novel content selection strategy. Our proposed content selection strategy is defined based on sample correlation of the curvelet transform coefficients. In our proposed fuzzy-based fusion scheme, the selection of curvelet coefficients are optimized by applying weighted averaging and maximum selection rules for the high frequency coefficients. For low frequency coefficients, the maximum selection rule based on local energy criterion is applied to better visual perception. Our proposed fusion algorithm is evaluated on a perfect brain angiography image dataset consisting of one hundred 2-D internal carotid rotational angiography videos. The obtained results demonstrate the effectiveness and efficiency of our proposed fusion algorithm in comparison with common and basic fusion algorithms.