1996
DOI: 10.1002/jmri.1880060303
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Quantification of white matter and gray matter volumes from T1 parametric images using fuzzy classifiers

Abstract: White matter (WM) and gray matter (GM) were accurately measured using a technique based on a single standardized fuzzy classifier (FC) for each tissue. Fuzzy classifier development was based on experts' visual assessments of WM and GM boundaries from a set of T1 parametric MR images. The fuzzy classifier method's accuracy was validated and optimized by a set of T1 phantom images that were based on hand-detailed human brain cryosection images. Nine sets of axial T1 images of varying thickness equally distribute… Show more

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Cited by 43 publications
(19 citation statements)
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“…In this application, constraints on the mapping from the high-dimensional feature (voxel) space to target class were imposed through massive weight sharing. Classifiers have played a prominent role in structural neuroimaging (e.g., Herndon et al, 1996) and are now an integral part of computational anatomy and segmentation schemes (e.g., Ashburner and Friston, 2005). However, classification schemes received little attention from the functional neuroimaging community until they were re-introduced in the context of mind-reading (Carlson et al, 2003;Cox and Savoy, 2003;Hanson et al, 2004;Haynes and Rees, 2005;Norman et al, 2006;Martinez-Ramon et al, 2006).…”
Section: Generative Recognition and Classification Modelsmentioning
confidence: 99%
“…In this application, constraints on the mapping from the high-dimensional feature (voxel) space to target class were imposed through massive weight sharing. Classifiers have played a prominent role in structural neuroimaging (e.g., Herndon et al, 1996) and are now an integral part of computational anatomy and segmentation schemes (e.g., Ashburner and Friston, 2005). However, classification schemes received little attention from the functional neuroimaging community until they were re-introduced in the context of mind-reading (Carlson et al, 2003;Cox and Savoy, 2003;Hanson et al, 2004;Haynes and Rees, 2005;Norman et al, 2006;Martinez-Ramon et al, 2006).…”
Section: Generative Recognition and Classification Modelsmentioning
confidence: 99%
“…Soft segmentations, on the other hand, retain more information from the original image by allowing for uncertainty (such as membership for every pixel) in the location of object boundaries. Generally, membership functions can be derived by fuzzy clustering and classifier algorithms (Herndon et al, 1996;Pham & Prince, 1999) or statistical algorithms, in which case the membership functions are probability functions (Wells III et al, 1996), or can be computed as estimates of partial volume fractions (Choi et al, 1991). Soft segmentations based on membership functions can be easily converted to hard segmentations by assigning a pixel to its class with the highest membership value (Pham et al, 2000).…”
Section: Wwwintechopencommentioning
confidence: 99%
“…Rather than exclusively classifying a voxel as belonging to a particular class, soft segmentation methods allow for a continuous grade of membership within different classes. These memberships can be computed using fuzzy clustering [1,2] or probabilistic classification alorithms [3,4]. This approach, however, does not explicitly model partial volume effects and can therefore be susceptible to certain artifacts in the resulting segmentation [5].…”
Section: Introductionmentioning
confidence: 99%