A multi-scale line filter with automatic scale selection based on the Hessian matrix for medical image segmentation

Lorenz, Cristian; Carlsen, Ingwer C.; Buzug, Thorsten M.; Fassnacht, Carola; Weese, Jürgen

doi:10.1007/3-540-63167-4_47

Cited by 76 publications

(51 citation statements)

References 11 publications

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“…After computing and sorting the eigenvalues according to magnitude, a curvilinear structure called line (in this paper: string) is detected when the two largest ones have (approximately) the same magnitude and sign (negative for bright lines, positive for dark lines) whereas the third one is close to zero. The geometrical mean Lorenz et al [41], [42] are using the Hessian of 3D second derivatives in a similar manner to what has been presented in this paper. After deriving the eigen values, their relative signs and magnitudes are exploited to distinguish between strings (called lines) and planes, although the given measure for "line-ness" seems to discriminate poorly against "plane-ness".…”

Section: Comparisons and References To Other Methodsmentioning

confidence: 94%

Efficient Detection of Second-Degree Variations in 2D and 3D Images

Danielsson¹,

Lin²,

Ye³

2001

Journal of Visual Communication and Image Representation

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Section: Comparisons and References To Other Methodsmentioning

confidence: 94%

Efficient Detection of Second-Degree Variations in 2D and 3D Images

Danielsson¹,

Lin²,

Ye³

2001

Journal of Visual Communication and Image Representation

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“…The second approach involves application of scale-space analysis based on vesselness filters, which can be used to directly visualize venous structures [7]. Segmentation is then done using thresholding [8] or an active contour model [9]. Some examples of vesselness filters [10][11][12][13] are based on Frangi's [7] and Sato's vesselness filter [8].…”

Section: Introductionmentioning

confidence: 99%

Application of a Mamdani-Type Fuzzy Rule-Based System to Segment Periventricular Cerebral Veins in Susceptibility-Weighted Images

Aymerich

Sobrevilla

Montseny

2016

Information Processing and Management of Uncertainty in Knowledge-Based Systems

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Abstract. This paper presents an algorithm designed to segment veins in the periventricular region of the brain in susceptibility-weighted magnetic resonance images. The proposed algorithm is based on a Mamdani-type fuzzy rule-based system that enables enhancement of veins within periventricular regions of interest as the first step. Segmentation is achieved after determining the cut-off value providing the best trade-off between sensitivity and specificity to establish the suitability of each pixel to belong to a cerebral vein. Performance of the algorithm in susceptibility-weighted images acquired in healthy volunteers showed very good segmentation, with a small number of false positives. The results were not affected by small changes in the size and location of the regions of interest. The algorithm also enabled detection of differences in the visibility of periventricular veins between healthy subjects and multiple sclerosis patients.

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“…Multiscale filtering has been used for the segmentation of curvilinear or tubular structures in 3D medical images. [6][7][8][9][10][11][12][13] The conventional multiscale filtering method 6,11,14 has been widely used to enhance vascular structures at variable sizes for vessel extraction. In this method, the images are convolved with 3D Gaussian filters at multiple scales and the eigenvalues of the Hessian matrix at each voxel are analyzed in terms of a response function to extract local structures in each scale of the filtered image.…”

Section: Introductionmentioning

confidence: 99%

Computerized analysis of coronary artery disease: Performance evaluation of segmentation and tracking of coronary arteries in CT angiograms

et al. 2014

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Purpose:The authors are developing a computer-aided detection system to assist radiologists in analysis of coronary artery disease in coronary CT angiograms (cCTA). This study evaluated the accuracy of the authors' coronary artery segmentation and tracking method which are the essential steps to define the search space for the detection of atherosclerotic plaques. Methods: The heart region in cCTA is segmented and the vascular structures are enhanced using the authors' multiscale coronary artery response (MSCAR) method that performed 3D multiscale filtering and analysis of the eigenvalues of Hessian matrices. Starting from seed points at the origins of the left and right coronary arteries, a 3D rolling balloon region growing (RBG) method that adapts to the local vessel size segmented and tracked each of the coronary arteries and identifies the branches along the tracked vessels. The branches are queued and subsequently tracked until the queue is exhausted. With Institutional Review Board approval, 62 cCTA were collected retrospectively from the authors' patient files. Three experienced cardiothoracic radiologists manually tracked and marked center points of the coronary arteries as reference standard following the 17-segment model that includes clinically significant coronary arteries. Two radiologists visually examined the computer-segmented vessels and marked the mistakenly tracked veins and noisy structures as false positives (FPs). For the 62 cases, the radiologists marked a total of 10191 center points on 865 visible coronary artery segments. Results: The computer-segmented vessels overlapped with 83.6% (8520/10191) of the center points. Relative to the 865 radiologist-marked segments, the sensitivity reached 91.9% (795/865) if a true positive is defined as a computer-segmented vessel that overlapped with at least 10% of the reference center points marked on the segment. When the overlap threshold is increased to 50% and 100%, the sensitivities were 86.2% and 53.4%, respectively. For the 62 test cases, a total of 55 FPs were identified by radiologist in 23 of the cases. Conclusions:The authors' MSCAR-RBG method achieved high sensitivity for coronary artery segmentation and tracking. Studies are underway to further improve the accuracy for the arterial segments affected by motion artifacts, severe calcified and noncalcified soft plaques, and to reduce the false tracking of the veins and other noisy structures. Methods are also being developed to detect coronary artery disease along the tracked vessels.

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A multi-scale line filter with automatic scale selection based on the Hessian matrix for medical image segmentation

Cited by 76 publications

References 11 publications

Efficient Detection of Second-Degree Variations in 2D and 3D Images

Efficient Detection of Second-Degree Variations in 2D and 3D Images

Application of a Mamdani-Type Fuzzy Rule-Based System to Segment Periventricular Cerebral Veins in Susceptibility-Weighted Images

Computerized analysis of coronary artery disease: Performance evaluation of segmentation and tracking of coronary arteries in CT angiograms

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