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

Abstract

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.

An automatic aneurysm extraction algorithm in fused brain digital subtraction angiography images

“…For this purpose, we use a state-of-the-art algorithm [15] that lately has been introduced to fuse a sequence of the DSA images. This automatic fuzzybased fusion algorithm can be summarized in two main steps [15]. In the preprocessing section, at first, registration operation was performed.…”

“…In the main process, Curvelet transform was used to decompose the valuable frames. In the following, low frequency coefficients of the valuable frames were fused based on the maximum local energy and their high frequency coefficients were combined based on the fuzzy logic rules [15].…”

“…To evaluate our algorithm, we collected 30 2-D fused DSA images from different views of the angiography operation based on the introduced fusion algorithm in [15]. Moreover, in each fused image, the aneurysm location was labeled by expert physician.…”

“…(15)). For this purpose, Table 1 presents the radius values and accuracy rate of the extracted aneurysm regions by our proposed algorithm for 10 different sample views.…”