TY - GEN
T1 - Tendinopathy discrimination using spatial frequency parameters and artificial neural networks
AU - Song, Pengfei
AU - Linstrom, Kristopher R.
AU - Boye, A. John
AU - Kulig, Kornelia
AU - Burnfield, Judith M.
AU - Bashford, Gregory R.
PY - 2009
Y1 - 2009
N2 - Healthy tendon's structural characteristics are related to the anisotropic speckle pattern observed in ultrasound images. This speckle orientation is disrupted upon damage to the tendon structure as observed in patients with tendinopathy. Quantification of the structural appearance of tendon shows promise in creating a tool for diagnosing, prognosing, or monitoring changes in tendon organization over time. Previously we showed the feasibility of using spatial frequency parameters and Linear Discriminant Analysis (LDA) to categorize tendon tissue as normal or tendinopathic with better than 80% accuracy. The current work aimed to improve accuracy by developing an Artificial Neural Network (ANN) classifier and compare its results with those achieved in our previous LDA work. The eight spatial frequency parameters used in our previous work were extracted from regions of interest (ROI) on tendon images, filtered and classified using an ANN classifier. The spatial frequency parameters were used as inputs to the ANN. For a tendon tested with an ANN trained for that type of tendon, the accuracy was very high, with a correct classification rate (CCR) of 95-99%. However, when testing a tendon with an ANN trained for a different tendon type, the CCR was only 72-75%. This seems to indicate that a unique ANN needs to be trained for each type of tendon. The high CCRs obtained using the tendon-specific ANNs suggest that this novel discrimination strategy may lead to a robust tool for diagnosing and monitoring a degenerated tendon's response to treatment. The ANN CCR was higher than the highest CCR of 82.6% obtained by the LDA used in our previous work.
AB - Healthy tendon's structural characteristics are related to the anisotropic speckle pattern observed in ultrasound images. This speckle orientation is disrupted upon damage to the tendon structure as observed in patients with tendinopathy. Quantification of the structural appearance of tendon shows promise in creating a tool for diagnosing, prognosing, or monitoring changes in tendon organization over time. Previously we showed the feasibility of using spatial frequency parameters and Linear Discriminant Analysis (LDA) to categorize tendon tissue as normal or tendinopathic with better than 80% accuracy. The current work aimed to improve accuracy by developing an Artificial Neural Network (ANN) classifier and compare its results with those achieved in our previous LDA work. The eight spatial frequency parameters used in our previous work were extracted from regions of interest (ROI) on tendon images, filtered and classified using an ANN classifier. The spatial frequency parameters were used as inputs to the ANN. For a tendon tested with an ANN trained for that type of tendon, the accuracy was very high, with a correct classification rate (CCR) of 95-99%. However, when testing a tendon with an ANN trained for a different tendon type, the CCR was only 72-75%. This seems to indicate that a unique ANN needs to be trained for each type of tendon. The high CCRs obtained using the tendon-specific ANNs suggest that this novel discrimination strategy may lead to a robust tool for diagnosing and monitoring a degenerated tendon's response to treatment. The ANN CCR was higher than the highest CCR of 82.6% obtained by the LDA used in our previous work.
KW - Artificial neural network
KW - Classifier
KW - Image analysis
KW - Tendinopathy
KW - Tendon
KW - Ultrasound
UR - http://www.scopus.com/inward/record.url?scp=77952836361&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77952836361&partnerID=8YFLogxK
U2 - 10.1109/ULTSYM.2009.5441973
DO - 10.1109/ULTSYM.2009.5441973
M3 - Conference contribution
AN - SCOPUS:77952836361
SN - 9781424443895
T3 - Proceedings - IEEE Ultrasonics Symposium
SP - 1902
EP - 1905
BT - 2009 IEEE International Ultrasonics Symposium and Short Courses, IUS 2009
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2009 IEEE International Ultrasonics Symposium, IUS 2009
Y2 - 20 September 2009 through 23 September 2009
ER -