Open AccessEdge sharpness assessment by parametric modeling: Application to magnetic resonance imaging
Author(s) -
Ahmad Rizwan,
Ding Yu,
Simonetti Orlando P.
Publication year - 2015
Publication title -
concepts in magnetic resonance part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.229
H-Index - 49
eISSN - 1552-5023
pISSN - 1546-6086
DOI - 10.1002/cmr.a.21339
Subject(s) - pixel , artificial intelligence , computer science , imaging phantom , computer vision , sigmoid function , enhanced data rates for gsm evolution , parametric statistics , magnetic resonance imaging , edge detection , metric (unit) , noise (video) , region of interest , image quality , medical imaging , line (geometry) , signal to noise ratio (imaging) , pattern recognition (psychology) , image (mathematics) , image processing , mathematics , physics , optics , statistics , medicine , telecommunications , operations management , geometry , artificial neural network , economics , radiology
In biomedical imaging, edge sharpness is an important yet often overlooked image quality metric. In this work, a semi‐automatic method to quantify edge sharpness is presented with application to magnetic resonance imaging (MRI). The method is based on parametric modeling of image edges. First, an edge map is automatically generated and one or more edges‐of‐interest (EOI) are manually selected using graphical user interface. Multiple exclusion criteria are then enforced to eliminate edge pixels that are potentially not suitable for sharpness assessment. Second, at each pixel of the EOI, an image intensity profile is read along a small line segment that runs locally normal to the EOI. Third, the profiles corresponding to all EOI pixels are individually fitted with a sigmoid function characterized by four parameters, including one that represents edge sharpness. Last, the distribution of the sharpness parameter is used to quantify edge sharpness. For validation, the method is applied to simulated data as well as MRI data from both phantom imaging and cine imaging experiments. This method allows for fast, quantitative evaluation of edge sharpness even in images with poor signal‐to‐noise ratio. Although the utility of this method is demonstrated for MRI, it can be adapted for other medical imaging applications. © 2015 Wiley Periodicals, Inc. Concepts Magn Reson Part A 44A: 138–149, 2015.