Evaluation of ridge seeking operators for multimodality medical image matching

Maintz, J. B. Antoine; Elsen, Petra A. van den; Viergever, Max A.

doi:10.1109/34.491617

Cited by 155 publications

(70 citation statements)

References 21 publications

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“…It is well known that the gradient at any point on such objects generally points towards the ridge and reverses its direction as it crosses the ridge [11,12]. Similarly, for a point to be on a ridge, it must be a local maximum on some direction, i.e., on a line passing through the point.…”

Section: Scan-conversion Algorithm In 2dmentioning

confidence: 99%

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Scan-Conversion Algorithm for Ridge Point Detection on Tubular Objects

Chang

Metaxas

Axel

2003

Lecture Notes in Computer Science

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Abstract. Anatomical structures contain various types of curvilinear or tube-like structures such as blood vessels and bronchial trees. In medical imaging, the extraction and representation of such structures are of clinical importance. Complex curvilinear structures can be best represented by their center lines (or skeletons) along their elongated direction. In this paper, a gradient-based method for ridge point extraction on tubular objects is presented. Using the gradients of distance maps or intensity profiles usually generates skeleton surfaces for 3D objects, which is not desirable for representing tubular objects. To extract only the points on the centerline, we first employ the gradient vector flow (GVF) technique and then apply eigenanalysis of the Hessian matrix to remove false positive points. We present various results of the method using CLSM (Confocal Laser Scanning Microscopy) images of blood fibrins and CT images of a skull and lungs. Our method is efficient and allows for completely automatic extraction of points along the centerline of a tubular object in its elongated direction.

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Section: Scan-conversion Algorithm In 2dmentioning

confidence: 99%

“…The first approach consists of extracting ridge points and connecting them in a postprocessing step [7,9,12,16]. Since it usually uses purely local criteria, this approach generates false positives for ridge points.…”

Section: Previous Workmentioning

confidence: 99%

Scan-Conversion Algorithm for Ridge Point Detection on Tubular Objects

Chang

Metaxas

Axel

2003

Lecture Notes in Computer Science

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“…Here, we use blood vessel ridges as a specific feature [9]. Defined as points where the image has an extremum in the direction of the largest surface curvature [10], ridges are a natural feature of blood vessels and are usually approximately center lines of blood vessels.…”

Section: B Develop 3d Registration Algorithm For Monitoring Disease mentioning

confidence: 99%

Development and Application of Advanced Ophthalmic Imaging Technology to Enhance Military Ocular Health Capabilities

Jiao¹

2008

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“…In our case, the weight matrix in (7) is given by W ;1 = d i a g f 1 : : : n g (10) and is a block-diagonal matrix. Note, that the i represent the localization errors of two corresponding landmarks.…”

Section: Estimation Of Landmark Localization Uncertaintiesmentioning

confidence: 99%

Image registration based on thin-plate splines and local estimates of anisotropic landmark localization uncertainties

Rohr

1998

Medical Image Computing and Computer-Assisted Intervention — MICCAI’98

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Abstract. We p r e s e n t an approach to elastic registration of tomographic brain images which is based on thin-plate splines and takes into account landmark errors. The inclusion of error information is important in clinical applications since landmark extraction is always prone to error. In comparison to previous work, our approach can cope with anisotropic errors, which is signi cantly more realistic than dealing only with isotropic errors. In particular, it is now possible to include di erent t ypes of landmarks, e.g., quasi-landmarks at the outer contour of the brain. Also, we introduce an approach to estimate landmark localization uncertainties directly from the image data. Experimental results are presented for the registration of 2D and 3D MR images. MotivationImage registration is fundamental to computer-assisted neurosurgery. Examples are the registration of tomographic images and the registration of images with digital atlases. In either case, the central aim is to increase the accuracy of localizing anatomical structures in 3D space. One principal approach to registration is based on thin-plate splines and anatomical point landmarks.Previous work on thin-plate spline registration has concentrated on specifying the landmarks manually and on using an interpolating transformation model (e.g., 2], 6], 11]). The approach is e cient and well-suited for user-interaction which w e consider important in clinical scenaria. However, with an interpolation scheme the corresponding landmarks are forced to match exactly. I t i s ( i m p l i citly) assumed that the landmark positions are known exactly, w h i c h, however, is generally not true in practical applications. Therefore, to take i n to account landmark localization errors, we h a ve recently introduced an approximation scheme ( 13]). This scheme is based on a minimizing functional, it uses scalar weights to represent landmark errors, and it has been described for images of arbitrary dimensions. The applicability of our approach has been demonstrated for 2D MR images. Bookstein 3] has proposed a di erent approach to relax the interpolation conditions. His approach, however, has not been related to a minimizing

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Evaluation of ridge seeking operators for multimodality medical image matching

Cited by 155 publications

References 21 publications

Scan-Conversion Algorithm for Ridge Point Detection on Tubular Objects

Scan-Conversion Algorithm for Ridge Point Detection on Tubular Objects

Development and Application of Advanced Ophthalmic Imaging Technology to Enhance Military Ocular Health Capabilities

Image registration based on thin-plate splines and local estimates of anisotropic landmark localization uncertainties

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