Automated Fusion of Multi-modality Biomedical Images. Este artículo no está disponible.Automated Fusion of Multi-modality Biomedical Images

A novel automated approach of multi-modality biomedical image control point detection, registration, and fusion has been successfully developed. The new algorithm, which consists of the Adaptive Exploratory Algorithm for the control point/feature detection and Heuristic Optimization Algorithm, is reliable and time efficient. The new approach has been applied on three ophthalmologic modalities of nonhuman primate eyes - angiogram, fundus, and oxygen saturation retinal images. It has achieved an excellent result by giving the visualization of fundus or oxygen saturation image with a complete angiogram overlay. By locking the multi-sensor retinal images in one place, the algorithm allows ophthalmologists to match the same eye over time to get a sense of disease progress and pinpoint surgical tools. The new registration and fusion algorithm can be easily expanded to the eye, brain, or body images of human or animals’. The target readers are graduate/undergraduate students, faculty, and research scientists who are interested in the image registration and fusion algorithms; ophthalmologists and physicians; image processing software developers; and whoever wants to learn image fusion.

A novel automated approach of multi-modality biomedical image control point detection, registration, and fusion has been successfully developed. The new algorithm, which consists of the Adaptive Exploratory Algorithm for the control point/feature detection and Heuristic Optimization Algorithm, is reliable and time efficient. The new approach has been applied on three ophthalmologic modalities of nonhuman primate eyes - angiogram, fundus, and oxygen saturation retinal images. It has achieved an excellent result by giving the visualization of fundus or oxygen saturation image with a complete angiogram overlay. By locking the multi-sensor retinal images in one place, the algorithm allows ophthalmologists to match the same eye over time to get a sense of disease progress and pinpoint surgical tools. The new registration and fusion algorithm can be easily expanded to the eye, brain, or body images of human or animals'. The target readers are graduate/undergraduate students, faculty, and research scientists who are interested in the image registration and fusion algorithms; ophthalmologists and physicians; image processing software developers; and whoever wants to learn image fusion.