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Author
Title Clubfoot Image Classification
URL
Publication Date
Degree MS
Discipline/Department Biomedical Engineering
Degree Level masters
University/Publisher University of Iowa
Abstract Clubfoot is a congenital foot disorder that, left untreated, can limit a person's mobility by making it difficult and painful to walk. Although inexpensive and reliable treatment exists, clubfoot often goes untreated in the developing world, where 80% of cases occur. Many nonprofit and non-governmental organizations are partnering with hospitals and clinics in the developing world to provide treatment for patients with clubfoot, and to train medical personnel in the use of these treatment methods. As a component of these partnerships, clinics and hospitals are collecting patient records. Some of this patient information, such as photographs, requires expert quality assessment. Such assessment may occur at a later date by a staff member in the hospital, or it may occur in a completely different location through the web interface. Photographs capture the state of a patient at a specific point in time. If a photograph is not taken correctly, and as a result, has no clinical utility, the photograph cannot be recreated because that moment in time has passed. These observations have motivated the desire to perform real-time classification of clubfoot images as they are being captured in a possibly remote and challenging environment. In the short term, successful classification could provide immediate feedback to those taking patient photos, helping to ensure that the image is of good quality and the foot is oriented correctly at the time of image capture. In the long term, this classification could be the basis for automated image analysis that could reduce the workload of a busy staff, and enable broader provision of treatment.
Subjects/Keywords Classification; Clubfoot; Developing World; Machine Learning; Point-of-care; Talipes; Biomedical Engineering and Bioengineering
Contributors Casavant, Thomas L. (supervisor)
Language en
Country of Publication us
Format application/pdf
Record ID oai:ir.uiowa.edu:etd-4836
Repository iowa
Date Retrieved
Date Indexed 2019-11-13
Grantor University of Iowa
Issued Date 2013-07-01 07:00:00
Note <p>This thesis has been optimized for improved web viewing. If you require the original version, contact the University Archives at the University of Iowa: <a href="https://www.lib.uiowa.edu/sc/contact/">https://www.lib.uiowa.edu/sc/contact/</a>.</p>

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…Table 17: Three majority classifiers were used in the Quality on Quality classification scheme to predict an image’s orientation................................................................................... 36   Table 18: Three Naïve Bayes…

…classifiers were used in the Quality on Quality classification scheme to predict an image’s orientation given a PHOW feature vector that was generated with a bag-of-words containing 600 features............................................ 36   Table 19…

…Three SMO classifiers were used in the Quality on Quality classification scheme to predict an image’s orientation given a PHOW feature vector that was generated with a bag-of-words containing 600 features…

…37   Table 20: Naïve Bayes classifiers were used in the quality-based classification scheme to predict an image’s quality and orientation given a PHOW feature vector that was generated with a bag-of-words containing 600 features…

…39   Table 21: SMO classifiers were used in the quality-based classification scheme to predict an image’s quality and orientation given a PHOW feature vector that was generated with a bag-of-words containing 600 features…

…majority classifiers were used in the voting classification scheme to predict an image’s quality and orientation. ............................................................................... 42   Table 24: Naïve Bayes classifiers were used in the…

…voting classification scheme to predict an image’s quality and orientation given a PHOW feature vector that was generated with a bag-of-words containing 600 features. ............................................................. 42   Table 25: SMO…

…classifiers were used in the voting classification scheme to predict an image’s quality and orientation given a PHOW feature vector that was generated with a bag-of-words containing 600 features…

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