Vesselness based feature extraction for endoscopic image analysis

Lin, Bingxiong; Sun, Yu; Sánchez, J.; Qian, Xiaoning

doi:10.1109/isbi.2014.6868114

Cited by 6 publications

(8 citation statements)

References 10 publications

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“…Many state-of-the art branching point detectors [40], [41], vessel detection methods [33], [42], general feature point detectors have been proposed in the community. Among them, the following feature detectors were chosen based on the reports in [13], [17] and the availability of codes: VBCT [28], AFD [17], DoG [19], Hessian-Affine [18], [31], FAST [14], and likelihood ratio vesselness (refered to as "Sofka" in this paper) [42]. Note that VBCT, RBCT, and Sofka were branching point detectors.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Note that a similar idea of using peaks for vessel segmentation has been presented in image crawlers [35], [36]. Similar to VBCT [28], multiple tests are employed at each pixel p on the circle: 1) p should be bright on the ridgeness image (R(p) > R peak ); 2) p should have similar intensity with the center pixel (|I(p)−I(center)| < I similar ); and 3) the middle point p m of two peaks should be black (R(p m ) = 0). 4) the number of peaks should be three or four.…”

Section: Branching Point Detection (Rbct)mentioning

confidence: 97%

“…Note that branching segments are essentially curve segments and a pair of branch segment correspondence can generate tens of pairs of point correspondences. Our previous work in [28] proposed vesselness based circle test (VBCT) and vesselness based branching segment detection (VBSD) to extract the two types of vessel features based on Frangi vesselness [29]. This work proposes a new way of blood vessel enhancement, based on a new ridgeness measure (Section II-B), which provides more accurate vessel localizations.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Vessel Feature Detection for Endoscopic Image Analysis

Lin

Sun

Sánchez

et al. 2015

IEEE Trans. Biomed. Eng.

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Distinctive feature detection is an essential task in computer-assisted minimally invasive surgery (MIS). For special conditions in an MIS imaging environment, such as specular reflections and texture homogeneous areas, the feature points extracted by general feature point detectors are less distinctive and repeatable in MIS images. We observe that abundant blood vessels are available on tissue surfaces and can be extracted as a new set of image features. In this paper, two types of blood vessel features are proposed for endoscopic images: branching points and branching segments. Two novel methods, ridgeness-based circle test and ridgeness-based branching segment detection are presented to extract branching points and branching segments, respectively. Extensive in vivo experiments were conducted to evaluate the performance of the proposed methods and compare them with the state-of-the-art methods. The numerical results verify that, in MIS images, the blood vessel features can produce a large number of points.More importantly, those points are more robust and repeatable than the other types of feature points. In addition, due to the difference in feature types, vessel features can be combined with other general features, which makes them new tools for MIS image analysis. These proposed methods are efficient and the code and datasets are made available to the public.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Branching Point Detection (Rbct)mentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Efficient Vessel Feature Detection for Endoscopic Image Analysis

Lin

Sun

Sánchez

et al. 2015

IEEE Trans. Biomed. Eng.

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“…Bartoli provided a uterus dataset, which contains tissue deformation caused by instrument interactions. In an image dataset was collected for evaluation of the repeatability of feature detectors. The dataset in contains hundreds of images sampled from in vivo videos taken during colon surgeries; the images in this dataset were taken at different viewpoints, and the ground truth homography mappings are available.…”

Section: Methodsmentioning

confidence: 99%

“…The difficulties are mainly from the special environment of the abdominal MIS. First, compared with general images taken in a man‐made environment, MIS images usually contain homogeneous areas and specular reflections, due to the smooth and wet tissue surface . These properties significantly affect the performance of state‐of‐the‐art feature point detection methods.…”

Section: Introductionmentioning

confidence: 99%

Video‐based 3D reconstruction, laparoscope localization and deformation recovery for abdominal minimally invasive surgery: a survey

Lin

Sun

Qian

et al. 2015

Int J Med Robotics Comput Assist Surg

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SPSVO: a self-supervised surgical perception stereo visual odometer for endoscopy

Zhao,

Luo,

et al. 2023

Robotica

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Accurate tracking and reconstruction of surgical scenes is a critical enabling technology toward autonomous robotic surgery. In endoscopic examinations, computer vision has provided assistance in many aspects, such as aiding in diagnosis or scene reconstruction. Estimation of camera motion and scene reconstruction from intra-abdominal images are challenging due to irregular illumination and weak texture of endoscopic images. Current surgical 3D perception algorithms for camera and object pose estimation rely on geometric information (e.g., points, lines, and surfaces) obtained from optical images. Unfortunately, standard hand-crafted local features for pose estimation usually do not perform well in laparoscopic environments. In this paper, a novel self-supervised Surgical Perception Stereo Visual Odometer (SPSVO) framework is proposed to accurately estimate endoscopic pose and better assist surgeons in locating and diagnosing lesions. The proposed SPSVO system combines a self-learning feature extraction method and a self-supervised matching procedure to overcome the adverse effects of irregular illumination in endoscopic images. The framework of the proposed SPSVO includes image pre-processing, feature extraction, stereo matching, feature tracking, keyframe selection, and pose graph optimization. The SPSVO can simultaneously associate the appearance of extracted feature points and textural information for fast and accurate feature tracking. A nonlinear pose graph optimization method is adopted to facilitate the backend process. The effectiveness of the proposed SPSVO framework is demonstrated on a public endoscopic dataset, with the obtained root mean square error of trajectory tracking reaching 0.278 to 0.690 mm. The computation speed of the proposed SPSVO system can reach 71ms per frame.

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Vesselness based feature extraction for endoscopic image analysis

Cited by 6 publications

References 10 publications

Efficient Vessel Feature Detection for Endoscopic Image Analysis

Efficient Vessel Feature Detection for Endoscopic Image Analysis

Video‐based 3D reconstruction, laparoscope localization and deformation recovery for abdominal minimally invasive surgery: a survey

SPSVO: a self-supervised surgical perception stereo visual odometer for endoscopy

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