Labeling of Baropodometric Analysis Data Using Computer Vision Techniques in Classification of Foot Deformities
<i>Background and Objectives</i>: Foot deformities are the basis of numerous disorders of the locomotor system. An optimized method of classification of foot deformities would enable an objective identification of the type of deformity since the current assessment methods do not show an...
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MDPI AG
2023-04-01
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| Series: | Medicina |
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| Online Access: | https://www.mdpi.com/1648-9144/59/5/840 |
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| author | Siniša S. Babović Mia Vujović Nebojša P. Stilinović Ostoja Jeftić Aleksa D. Novaković |
| author_facet | Siniša S. Babović Mia Vujović Nebojša P. Stilinović Ostoja Jeftić Aleksa D. Novaković |
| author_sort | Siniša S. Babović |
| collection | DOAJ |
| description | <i>Background and Objectives</i>: Foot deformities are the basis of numerous disorders of the locomotor system. An optimized method of classification of foot deformities would enable an objective identification of the type of deformity since the current assessment methods do not show an optimal level of objectivity and reliability. The acquired results would enable an individual approach to the treatment of patients with foot deformities. Thus, the goal of this research study was the development of a new, objective model for recognizing and classifying foot deformities with the application of machine learning, by labeling baropodometric analysis data using computer vision methods. <i>Materials and Methods</i>: In this work, data from 91 students of the Faculty of Medicine and the Faculty of Sports and Physical Education, University of Novi Sad were used. Measurements were determined by using a baropodometric platform, and the labelling process was carried out in the Python programming language, using functions from the OpenCV library. Segmentation techniques, geometric transformations, contour detection and morphological image processing were performed on the images, in order to calculate the arch index, a parameter that gives information about the type of the foot deformity. <i>Discussion</i>: The foot over which the entire labeling method was applied had an arch index value of 0.27, which indicates the accuracy of the method and is in accordance with the literature. On the other hand, the method presented in our study needs further improvement and optimization, since the results of the segmentation techniques can vary when the images are not consistent. <i>Conclusions</i>: The labeling method presented in this work provides the basis for further optimization and development of a foot deformity classification system. |
| first_indexed | 2024-03-11T03:31:11Z |
| format | Article |
| id | doaj.art-4a003384a744457c88b34a8c2f1f8428 |
| institution | Directory Open Access Journal |
| issn | 1010-660X 1648-9144 |
| language | English |
| last_indexed | 2024-03-11T03:31:11Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Medicina |
| spelling | doaj.art-4a003384a744457c88b34a8c2f1f84282023-11-18T02:20:59ZengMDPI AGMedicina1010-660X1648-91442023-04-0159584010.3390/medicina59050840Labeling of Baropodometric Analysis Data Using Computer Vision Techniques in Classification of Foot DeformitiesSiniša S. Babović0Mia Vujović1Nebojša P. Stilinović2Ostoja Jeftić3Aleksa D. Novaković4Department of Anatomy, Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, SerbiaChair of Telecommunications and Signal Processing, Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, SerbiaDepartment of Pharmacology, Toxicology and Clinical Pharmacology, Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, SerbiaChair of Telecommunications and Signal Processing, Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, SerbiaDepartment of Anatomy, Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia<i>Background and Objectives</i>: Foot deformities are the basis of numerous disorders of the locomotor system. An optimized method of classification of foot deformities would enable an objective identification of the type of deformity since the current assessment methods do not show an optimal level of objectivity and reliability. The acquired results would enable an individual approach to the treatment of patients with foot deformities. Thus, the goal of this research study was the development of a new, objective model for recognizing and classifying foot deformities with the application of machine learning, by labeling baropodometric analysis data using computer vision methods. <i>Materials and Methods</i>: In this work, data from 91 students of the Faculty of Medicine and the Faculty of Sports and Physical Education, University of Novi Sad were used. Measurements were determined by using a baropodometric platform, and the labelling process was carried out in the Python programming language, using functions from the OpenCV library. Segmentation techniques, geometric transformations, contour detection and morphological image processing were performed on the images, in order to calculate the arch index, a parameter that gives information about the type of the foot deformity. <i>Discussion</i>: The foot over which the entire labeling method was applied had an arch index value of 0.27, which indicates the accuracy of the method and is in accordance with the literature. On the other hand, the method presented in our study needs further improvement and optimization, since the results of the segmentation techniques can vary when the images are not consistent. <i>Conclusions</i>: The labeling method presented in this work provides the basis for further optimization and development of a foot deformity classification system.https://www.mdpi.com/1648-9144/59/5/840arch indexsegmentationmachine learningbaropodometry |
| spellingShingle | Siniša S. Babović Mia Vujović Nebojša P. Stilinović Ostoja Jeftić Aleksa D. Novaković Labeling of Baropodometric Analysis Data Using Computer Vision Techniques in Classification of Foot Deformities Medicina arch index segmentation machine learning baropodometry |
| title | Labeling of Baropodometric Analysis Data Using Computer Vision Techniques in Classification of Foot Deformities |
| title_full | Labeling of Baropodometric Analysis Data Using Computer Vision Techniques in Classification of Foot Deformities |
| title_fullStr | Labeling of Baropodometric Analysis Data Using Computer Vision Techniques in Classification of Foot Deformities |
| title_full_unstemmed | Labeling of Baropodometric Analysis Data Using Computer Vision Techniques in Classification of Foot Deformities |
| title_short | Labeling of Baropodometric Analysis Data Using Computer Vision Techniques in Classification of Foot Deformities |
| title_sort | labeling of baropodometric analysis data using computer vision techniques in classification of foot deformities |
| topic | arch index segmentation machine learning baropodometry |
| url | https://www.mdpi.com/1648-9144/59/5/840 |
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