NDLI: Delineation of curvilinear structures on magnetic images using eigenvalues of Hessian based vesselness filter

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Delineation of curvilinear structures on magnetic images using eigenvalues of Hessian based vesselness filter

Content Provider	Semantic Scholar
Author	Hassan, Hassan Hasliza Goussev, Serguei
Copyright Year	2018
Abstract	One of the main objectives of using magnetic data in geological interpretation is to delineate concealed geological structures that are often displayed as linear or curvilinear anomalies on magnetic images. These curvilinear anomalies are attributable to faults and fractures that are most often formed as a result of juxtaposition of rocks with contrasting magnetic susceptibility values. Therefore, accurate delineation of curvilinear geological structures is vital for oil and gas exploration as well as for obtaining a reliable interpretation of magnetic data. In this study a new 2D/3D Hessian based filtering technique called 'vesselness' is used to delineate curvilinear geological structures on magnetic images. This relatively new filtering technique was developed in 1998 by Frangi to delineate tube-shaped blood vessels on medical images and since then it has been used extensively in several disciplines. Even though blood vessels and geological structures are different in principle they are similar in concept. Unlike traditional intensity-based filters such as the horizontal gradient, vesselness is a structure-based filter and it relies on the shape of the structure. Traditional filters whose responses are independent of the shape of the structures rely on the intensity contrast of the structure and they often fail to delineate structures with low intensity contrasts (for example, weak or narrow structures). Vesselness filter uses the eigenvalues (λ1, λ2 and λ3) computed by spectral decomposition of a 2D/3D Hessian matrix of an image.
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://geoconvention.com/wp-content/uploads/abstracts/2018/184_GC2018_Delineation_of_curvilinear_structures_on_magnetic_images.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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