Improving the Hough Transform Through Three New Morphological Operators

Baykal, Ibrahim Cem

doi:10.1109/idap.2019.8875918

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…First, the fascicle ROI was filtered with a Frangi vesselness filter [13], [14] as described in the previous paragraph, with the only difference being the use of a smaller scale range to allow for identifying the finer vessel structures of the muscle fascicles (Figure 2E). Second, the fascicle ROI was stretched vertically to magnify the length of the fascicle lines within the ROI, as Hough transform is known to perform better for longer lines [16]. Since stretching a ROI beyond three times its original dimension can lead to gaps in the magnified lines [16], the fascicle ROI was stretched by a factor three, using bicubic interpolation.…”

Section: Methodsmentioning

confidence: 99%

“…Second, the fascicle ROI was stretched vertically to magnify the length of the fascicle lines within the ROI, as Hough transform is known to perform better for longer lines [16]. Since stretching a ROI beyond three times its original dimension can lead to gaps in the magnified lines [16], the fascicle ROI was stretched by a factor three, using bicubic interpolation. Third, the Hough transform was applied to the filtered and stretched fascicle ROI.…”

Section: Methodsmentioning

confidence: 99%

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A Hybrid Method for Ultrasound-Based Tracking of Skeletal Muscle Architecture

Verheul

Yeo

2022

Preprint

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Tracking skeletal muscle architecture using B-mode ultra-sound is a widely used method in the field of human movement science and biomechanics. Sequential methods based on optical flow algorithms allow for smooth and coherent muscle tracking but are known to drift over time. Non-sequential feature detection methods on the other hand, do not suffer from drift, but are limited to tracking only lower-dimensional features. They are also known to be sensitive to image noise, and therefore often result in highly irregular tracking patterns. Building on the complimentary nature of both approaches, we present a novel fully automated hybrid muscle tracking approach that combines a sequential feature-point tracking method and a non-sequential method based on Hough transform. Tibialis anterior fascicle pennation angle and length, and central aponeurosis displacement, were measured in five healthy individuals during isometric contractions at five different ankle angles. Our hybrid method was demonstrated to significantly (p < 0.001) reduce drift compared to two sequential methods, and curve irregularity was significantly (p < 0.001) decreased compared to a non-sequential method. These findings suggest that the proposed hybrid approach can uniquely mitigate drift and irregularity limitation of individual methods used for tracking skeletal muscle architecture. Fully automated muscle tracking allows for convenient analysis of large datasets, whereas automatic drift correction opens the door for tracking muscle architecture in long ultrasound recordings during common movements, such as walking, running, and jumping without the need for manual intervention.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A Hybrid Method for Ultrasound-Based Tracking of Skeletal Muscle Architecture

Verheul

Yeo

2022

Preprint

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show abstract

Section: B Hybrid Muscle Trackingmentioning

confidence: 99%

“…Since stretching a ROI beyond three times its original dimension can lead to gaps in the magnified lines [16], the fascicle ROI was stretched by a factor three, using bicubic interpolation. Third, the Hough transform was applied to the filtered and stretched fascicle ROI.…”

Section: B Hybrid Muscle Trackingmentioning

confidence: 99%

A Hybrid Method for Ultrasound-Based Tracking of Skeletal Muscle Architecture

Verheul

Yeo

2023

IEEE Trans. Biomed. Eng.

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Tracking skeletal muscle architecture using B-mode ultrasound is a widely used method in the field of human movement science and biomechanics. Sequential methods based on optical flow algorithms allow for smooth and coherent muscle tracking but are known to drift over time. Non-sequential feature detection methods on the other hand, do not suffer from drift, but are limited to tracking only lower-dimensional features. They are also known to be sensitive to image noise, and therefore often result in highly irregular tracking patterns. Building on the complimentary nature of both approaches, we present a novel automated hybrid muscle tracking approach that combines a sequential feature-point tracking method and a non-sequential method based on Hough transform. Methods: Tibialis anterior fascicle pennation angle and length, and central aponeurosis displacement, were measured in five healthy individuals during isometric contractions at five different ankle angles. Results: Our hybrid method was demonstrated to significantly (p < 0.001) reduce drift compared to two sequential methods, and curve irregularity was significantly (p < 0.001) decreased compared to a non-sequential method. Conclusion: These findings suggest that the proposed hybrid approach can uniquely mitigate drift and irregularity limitation of individual methods used for tracking skeletal muscle architecture. Significance: Automated muscle tracking allows for convenient analysis of large datasets, whereas automatic drift correction opens the door for tracking muscle architecture in long ultrasound recordings during common movements, such as walking, running, and jumping without the need for manual intervention.

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Ultrafast line detector

Yılmaz

Baykal

2022

J. Electron. Imag.

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Improving the Hough Transform Through Three New Morphological Operators

Cited by 3 publications

References 13 publications

A Hybrid Method for Ultrasound-Based Tracking of Skeletal Muscle Architecture

A Hybrid Method for Ultrasound-Based Tracking of Skeletal Muscle Architecture

A Hybrid Method for Ultrasound-Based Tracking of Skeletal Muscle Architecture

Ultrafast line detector

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