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. 2017 Oct 27;12(10):e0187042.
doi: 10.1371/journal.pone.0187042. eCollection 2017.

A medical imaging analysis system for trigger finger using an adaptive texture-based active shape model (ATASM) in ultrasound images

Bo-I Chuang  1 , Li-Chieh Kuo  2 , Tai-Hua Yang  3 , Fong-Chin Su  3 , I-Ming Jou  4 , Wei-Jr Lin  3 , Yung-Nien Sun  1
Affiliations

A medical imaging analysis system for trigger finger using an adaptive texture-based active shape model (ATASM) in ultrasound images

Bo-I Chuang et al. PLoS One. .

Abstract

Trigger finger has become a prevalent disease that greatly affects occupational activity and daily life. Ultrasound imaging is commonly used for the clinical diagnosis of trigger finger severity. Due to image property variations, traditional methods cannot effectively segment the finger joint's tendon structure. In this study, an adaptive texture-based active shape model method is used for segmenting the tendon and synovial sheath. Adapted weights are applied in the segmentation process to adjust the contribution of energy terms depending on image characteristics at different positions. The pathology is then determined according to the wavelet and co-occurrence texture features of the segmented tendon area. In the experiments, the segmentation results have fewer errors, with respect to the ground truth, than contours drawn by regular users. The mean values of the absolute segmentation difference of the tendon and synovial sheath are 3.14 and 4.54 pixels, respectively. The average accuracy of pathological determination is 87.14%. The segmentation results are all acceptable in data of both clear and fuzzy boundary cases in 74 images. And the symptom classifications of 42 cases are also a good reference for diagnosis according to the expert clinicians' opinions.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Tendon and synovial sheath in the finger ultrasound image.
(a) acquired original image; (b) tendon (solid line), synovial sheath (broken line) area and surrounding tissues.
Fig 2
Fig 2. Different characteristics of edge at the synovial sheath (top region) and the volar plate (bottom region).
(a) better image quality; (b) worse image quality.
Fig 3
Fig 3. ATASM Flowchart.
Fig 4
Fig 4. Landmark points of the training data.
Fig 5
Fig 5. Texture images with different settings.
(a) original image; (b) imaginary section of Gabor with 0 degrees; (c) level-edge part of Laws’ texture energy measure images.
Fig 6
Fig 6. Procedure for intensity template training.
Fig 7
Fig 7. Boundary of the synovial sheath.
(a) original image; (b) the lower part and upper parts of the synovial sheath boundary; (c) complete boundary after interpolation.
Fig 8
Fig 8. Resulting tendon segmentation images with different methods.
The ground truths are indicated in white and the segmentation results are indicated in red. The first three rows are the tendons with clear boundaries and the remaining rows are with fuzzy boundaries.
Fig 9
Fig 9. Image results for synovial sheath segmentation.
The first column is the original images, and the second column is the segmented results (red line) and ground truth (white line).
Fig 10
Fig 10. Model matching results.
(a) image with clear boundary; (b) image with fuzzy boundary.
Fig 11
Fig 11. Special cases of finger image.
(a) less synovial sheath area; (b) contains multiple tendons.
See this image and copyright information in PMC

References

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