With advances in technology, hyperspectral imaging has become an emerging area of research due to its numerous advantages over conventional imaging techniques. HyperSpectral (HS) cameras generate images of high spectral as well as spatial resolution. Hence, HS images carry much more information from the scene than the conventional red, green and blue (RGB) images. This inspired researchers to use HS technologies for many different applications ranging from crime investigations to crop monitoring. It is important to accurately detect veins during surgical treatments, but this often turns out to be difficult. Wrongly locating veins or anatomical variations could result in accidental injury to blood vessels. This could lead to a longer operation time or even create serious complications. Furthermore, for majority of medical procedures, it is necessary to accurately define the location of veins. Over the past years, various methods including near infrared (NIR) and multi-spectral image processing-based methods have been proposed to help with detecting and accurately locating the veins. However, the performance of these methods is limited and demand for more accurate and convenient methods are increasing. HS images are two-dimensional (2D) representation of the scene at many light spectral. This brings the challenge of processing high dimensional data, which require significant processing power to deal with them. Various methods such as principal component analysis (PCA), Moving Window-PCA and Folded-PCA, which are widely used to reduce the dimensionality of HS image data, are reviewed in this book chapter. Conventional RGB, HS, NIR and multispectral images are studied and then HS imaging systems are introduced. Different applications of HS imaging are reviewed and their potential for vein detection is highlighted. Different techniques for reducing high dimensional data are discussed, and finally, different vein detection methods and some of the existing vein benchmark datasets are also introduced.
|Title of host publication
|Computer Vision: Applications of Visual AI and Image Processing
|Pancham Shukla, Rajanikanth Aluvalu, Shilpa Gite, Uma Maheswari
|Walter de Gruyter
|Number of pages
|Published - 20 Feb 2023
|De Gruyter Frontiers in Computational Intelligence