Accurate Quantification of Small-Diameter Tubular Structures in Isotropic CT Volume Data Based on Multiscale Line Filter Responses

Sato, Yoshinobu; Yamamoto, Saburo; Tamura, Shinichi

doi:10.1007/978-3-540-30135-6_62

Cited by 10 publications

(12 citation statements)

References 11 publications

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“…This wide range of techniques closely corresponds to the suite of methods that have been applied throughout the medical image analysis literature [28]. Of particular note, the recent literature has been dominated by Hessian-based methods because of their utility in characterizing the elongated structure of vessels [3,13,36,49,67].…”

Section: Introductionmentioning

confidence: 91%

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Retinal Vessel Centerline Extraction Using Multiscale Matched Filters, Confidence and Edge Measures

Sofka

Stewart

2006

IEEE Trans. Med. Imaging

310

184

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Motivated by the goals of improving detection of low-contrast and narrow vessels and eliminating false detections at non-vascular structures, a new technique is presented for extracting vessels in retinal images. The core of the technique is a new likelihood ratio test that combines matchedfilter responses, confidence measures and vessel boundary measures. Matched filter responses are derived in scale-space to extract vessels of widely varying widths. A vessel confidence measure is defined as a projection of a vector formed from a normalized pixel neighborhood onto a normalized ideal vessel profile. Vessel boundary measures and associated confidences are computed at potential vessel boundaries. Combined, these responses form a 6-dimensional measurement vector at each pixel. A training technique is used to develop a mapping of this vector to a likelihood ratio that measures the "vesselness" at each pixel. Results comparing this vesselness measure to matched filters alone and to measures based on the Hessian of intensities show substantial improvements both qualitatively and quantitatively. The Hessian can be used in place of the matched filter to obtain similar but less-substantial improvements or to steer the matched filter by preselecting kernel orientations. Finally, the new vesselness likelihood ratio is embedded into a vessel tracing framework, resulting in an efficient and effective vessel centerline extraction algorithm.

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Section: Introductionmentioning

confidence: 91%

“…We choose two specific measures here for our analysis, [13] and [34], which have been applied in a number of papers [3,49,51,56,57,67]. They both start from the definition of the scale-space…”

Section: Introductionmentioning

confidence: 99%

Retinal Vessel Centerline Extraction Using Multiscale Matched Filters, Confidence and Edge Measures

Sofka

Stewart

2006

IEEE Trans. Med. Imaging

310

184

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“…To recognize internal structures is essential for comprehensive CAD system. One of the purposes of this group is to develop analysis method of internal structures of organs and their modeling [3,4]. 2.…”

Section: Research Organizationmentioning

confidence: 99%

Opening Ceremony

2006

Int J CARS

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“…Early attempts have dealt with coronary structure extraction in MRA [27,28] and MSCT images [29,30]. The performances of these methods vary according to multiple factors among which the presence of severe pathologies (acute stenosis, large aneurysm [31,32]), local complexity of vascular networks (complicated branching situations) and the tissue environment into which they stand.…”

Section: Introductionmentioning

confidence: 99%