标题：A Novel Image Fusion Method Which Combines Wiener Filtering, Pulsed Chain Neural Networks and Discrete Wavelet Transforms for Medical Imaging Applications
期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2018
卷号：9
期号：4
页码：9-16
语种：English
出版社：Ayushmaan Technologies
摘要：Medical image fusion is an important tool for correct decision making in clinical diagnoses. It aims to increase image information content and provide complementary context for anatomical and physiological information by creating a single composite image from two or more source images. Even so, existing image fusion techniques do not completely resolve the inherent limitations of fused images, particularly when considering lost textural information and visual detail. This paper proposes a new technique that uses a space-variant Wiener filter followed by the enhancement of the filtered images with lateral inhibition and excitation in a feature-linking Pulse Coupled Neural Network (PCNN) under maximized normalization. The enhanced images are then fused using a Shift-Invariant Discrete Wavelet Transform (SIDWT). The resulting fused images are then evaluated with standard objective criteria and compared with results from previously existing image fusion methods, showing significant improvements in the main performance indicators.
