Formation of the magnetic domain structure and properties of magnetic cores under conditions of ordered laser deformations

Pudov, V. I.; Dragoshanskiĭ, Yu. N.

doi:10.1134/s1028335817090026

Cited by 3 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the CAA images, such centerline exists like gray extreme values of the perpendicular vessel's direction. To detect such ridge points, the image's gradient and the HM can be calculated [5]. Also, HM has been used to evaluate the blood vessels' characteristics with a multiscale vessel enhancement filtering for CA extraction [6].…”

Section: Introductionmentioning

confidence: 99%

Extraction of Blood Vessels Geometric Shape Features with Catheter Localization and Geodesic Distance Transform for Right Coronary Artery Detection.

Hawas

Elsetiha²,

El-Khobby³

et al. 2023

Journal of Engineering Research

View full text Add to dashboard Cite

X-ray angiography is considered the standard imaging sensory system for diagnosing coronary artery diseases. For automated, accurate diagnosis of such diseases, coronary vessels' detection from the captured low quality and noisy angiography images is challenging. It is essential to detect the main branch of the coronary artery, to resolve such limitations along with the problems due to the sudden changes in the lumen diameter, and the abrupt changes in local artery direction. Accordingly, this paper solved these limitations by proposing a computer-aided detection system for the right coronary artery (RCA) extraction, where geometric shape features with catheter localization and geodesic distance transform in the angiography images through two parts. In part 1, the captured image was initially preprocessed for contrast enhancement using singular value decomposition-based contrast adjustment, followed by generating the vesselness map using Jerman filter, and for further segmentation the K-means was introduced. Afterward, in part 2, the geometric shape features of the RCA, as well as the skeleton gradient transform, and the start/end points were determined to extract the main blood vessel of the RCA. The analysis of the skeletonize image was performed using Geodesic distance transform to examine all branches starting from the predetermined start point and cover the branching till the predefined end points. A ranking matrix, and the inverse of skeletonization were finally carried out to get the actual main branch. The performance of the proposed system was then evaluated using different evaluation metrics on the angiography images. The results validated the dominance of the suggested system for extracting the main vessel achieving Dice values improvements within the range 2% -30% compared to using the traditional methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Extraction of Blood Vessels Geometric Shape Features with Catheter Localization and Geodesic Distance Transform for Right Coronary Artery Detection.

Hawas

Elsetiha²,

El-Khobby³

et al. 2023

Journal of Engineering Research

View full text Add to dashboard Cite

show abstract

“…The ridge point is the local extremum of the image brightness in a certain direction, and it appears as the gray extreme value of the vertical blood vessel in the coronary artery image. Pudov and Dragoshanskii [12] used the gradient of the image and the Hessian matrix to give the definition of the ridge point. Breitenreicher et al [13] listed the expressions of Hessian matrices at ridge points under three ideal vascular model conditions.…”

Section: Introductionmentioning

confidence: 99%

Extraction Method of Coronary Artery Blood Vessel Centerline in CT Coronary Angiography

et al. 2019

View full text Add to dashboard Cite

Aiming at the problem that the traditional coronary artery centerline extraction method is computationally intensive and requires a large number of manual interventions, a fully automatic coronary artery centerline tracking extraction method is proposed. First, adaptive adaptation of coronary vessels was performed based on different Fresenius norms designed for coronary scale. A multi-scale differential operator is constructed based on the vascular gray-scale distribution to establish a discriminant function, thereby obtaining the initial position and tracking direction of the seed point. Then, the new ridge point and tracking direction are optimally detected within the local arc length of the initial direction to ensure that the correct ridge position can still be obtained with large error interference. Finally, the pseudo vascular centerline is removed using the vascular topology feature detection method. The experimental results show that the method can accurately extract the blood vessel center line, direction vector and other information in the coronary angiography image without manual intervention, and can be used in the computer-assisted diagnosis and treatment process of clinical cardiovascular disease. INDEX TERMS Extraction method, coronary artery blood, vessel Centerline, CT coronary angiography.

show abstract

Physical Mechanisms of Reverse Magnetization Jumps and Improvement of the Functional Characteristics of Ferromagnetics

Dragoshanskiĭ

Pudov

2018

Dokl. Phys.

View full text Add to dashboard Cite

Formation of the magnetic domain structure and properties of magnetic cores under conditions of ordered laser deformations

Cited by 3 publications

References 4 publications

Extraction of Blood Vessels Geometric Shape Features with Catheter Localization and Geodesic Distance Transform for Right Coronary Artery Detection.

Extraction of Blood Vessels Geometric Shape Features with Catheter Localization and Geodesic Distance Transform for Right Coronary Artery Detection.

Extraction Method of Coronary Artery Blood Vessel Centerline in CT Coronary Angiography

Physical Mechanisms of Reverse Magnetization Jumps and Improvement of the Functional Characteristics of Ferromagnetics

Contact Info

Product

Resources

About