[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125056-en":3,"doc-seo-125056-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125056,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Detection of Myofascial Trigger Points With Ultrasound Imaging and Machine Learning - Thesis","Myofascial Pain Syndrome (MPS) is a common chronic muscle pain disorder affecting 85–93% of patients in specialty pain clinics. It is marked by hard, palpable nodules called myofascial trigger points (MTrPs), which may be active or latent. Because pain quantification is subjective, existing care pathways can cause mistreatment or inappropriate medication use. This thesis develops a machine-learning pipeline to detect MTrPs from B-mode ultrasound using texture features via Gray Level Co-Occurrence Matrix (GLCM), with GPU-accelerated feature maps and deep learning models, and evaluates correlations with ultrasound elastography-derived stiffness.","Clemson University  \nTigerPrints  \n\n| All Theses | Theses |\n| --- | --- |\n| 12-2023\u003Cbr>Detection of Myofascial Trigger Points With Ultrasound Imaging and Machine Learning\u003Cbr>Benjamin Formby [bformby@clemson.edu](bformby@clemson.edu)\u003Cbr>Follow this and additional works at: [https://tigerprints.clemson.edu/all_theses](https://tigerprints.clemson.edu/all_theses)\u003Cbr> Part of the Other Computer Engineering Commons |  |\n\nRecommended Citation  \nFormby, Benjamin, \"Detection of Myofascial Trigger Points With Ultrasound Imaging and Machine Learning\" (2023) . All Theses. 4182.  \n[https://tigerprints.clemson.edu/all_theses/4182](https://tigerprints.clemson.edu/all_theses/4182)  \nThis Thesis is brought to you for free and open access by the Theses at TigerPrints. It has been accepted for inclusion in All Theses by an authorized administrator of TigerPrints. For more information, please contact [kokeefe@clemson.edu](kokeefe@clemson.edu).  \nDetection of Myofascial Trigger Points with Ultrasound Imaging and Machine Learning  \n\n| A Thesis\u003Cbr>Presented to\u003Cbr>the Graduate School of\u003Cbr>Clemson University |\n| --- |\n| In Partial Fulfillment\u003Cbr>of the Requirements for the Degree\u003Cbr>Master of Science\u003Cbr>Computer Engineering |\n| by\u003Cbr>Benjamin Greer Knox Formby\u003Cbr>December 2023 |\n\nAccepted by:  \nDr. Kuang-Ching Wang, Committee Chair Dr. Adam Hoover  \nDr. Yongkai Wu  \nAbstract  \nMyofascial Pain Syndrome (MPS) is a common chronic muscle pain disorder that affects a large portion of the global population, seen in 85-93% of patients in specialty pain clinics [10] . MPS is characterized by hard, palpable nodules caused by a stiffened taut band of muscle fibers. These nodules are referred to as Myofascial Trigger Points (MTrPs) and can be classified by two states: active MTrPs (A-MTrPs) and latent MtrPs (L-MTrPs) . Treatment for MPS involves massage therapy, acupuncture, and injections or painkillers. Given the subjectivity of patient pain quantification, MPS can often lead to mistreatment or drug misuse. A deterministic way to quantify the pain is needed for better diagnosis and treatment.  \nVarious medical imaging technologies have been used to try to find quantifiable and measurable biomarkers of MTrPs. Ultrasound imaging, with it’s accessibility and variety of modalities, has shown significant findings in identifying MTrPs. Elastography ultrasound, which is used for measuring stiffness in soft tissues, has shown that MTrPs tend to be stiffer than normal muscle tissue. Doppler ultrasound has shown that bloodflow velocities differ significantly in areas surrounding MTrPs. MTrPs have been identified in standard B-mode grayscale ultrasound, but have varying conclusions with some studies identifying them as dark hypoechoic blobs and other studies showing them as bright hyperechoic blobs. Despite these discoveries, there is a high variance among results with no correlations to severity or pain.  \nAs a step towards quantifying the pain associated with MTrPs, this work aims to introduce a machine learning approach using image processing with texture recognition to detect MTrPs in Bmode ultrasound. A texture recognition algorithm called Gray Level Co-Occurrence Matrix (GLCM) is used to extract texture features from the B-mode ultrasound image. Feature maps are generated to emphasize these texture features in an image format in anticipation that a deep convolutional neural network will be able to correlate the features with the presence of a MTrP. The GLCM feature maps  \nare compared to the elastography ultrasound to determine any correlations with muscle stiffness and then evaluated in the presence of MTrPs. The feature map generation is accelerated with a GPU-based implementation for the goal of real-time processing and inference of the machine learning model. Finally, two deep learning models are implemented to detect MTrPs comparing the effect of using GLCM feature maps of B-mode ultrasound to emphasize texture features for machine learning model inputs.  \nDe","cbCailO3VXW3gpac","https://ap.wps.com/l/cbCailO3VXW3gpac","pdf",3633800,1,75,"English","en",105,"# Abstract\n## Background and problem statement\n## Imaging technologies and limitations\n## Proposed machine learning approach\n## Experimental design and model evaluation","[{\"question\":\"What problem does this thesis address in myofascial pain syndrome?\",\"answer\":\"It targets the need for a deterministic, quantifiable method to detect myofascial trigger points (MTrPs), since subjective pain reporting can lead to mistreatment or drug misuse.\"},{\"question\":\"Which ultrasound modality and feature method are used to detect MTrPs?\",\"answer\":\"The approach uses B-mode ultrasound with texture features extracted through the Gray Level Co-Occurrence Matrix (GLCM), then generates feature maps for machine learning inputs.\"},{\"question\":\"How is muscle stiffness information incorporated into the evaluation?\",\"answer\":\"The thesis compares the GLCM-based feature maps against elastography ultrasound results to explore correlations with muscle stiffness and to assess performance in the presence of MTrPs.\"}]","Detection of Myofascial Trigger Points With Ultrasound Imaging and Machine Learning - Thesis | PDF",1785896384,189,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"detection-of-myofascial-trigger-points-with-ultrasound-imaging-and-machine-learning-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/detection-of-myofascial-trigger-points-with-ultrasound-imaging-and-machine-learning-thesis/125056/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does this thesis address in myofascial pain syndrome?","Question",{"text":75,"@type":76},"It targets the need for a deterministic, quantifiable method to detect myofascial trigger points (MTrPs), since subjective pain reporting can lead to mistreatment or drug misuse.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which ultrasound modality and feature method are used to detect MTrPs?",{"text":80,"@type":76},"The approach uses B-mode ultrasound with texture features extracted through the Gray Level Co-Occurrence Matrix (GLCM), then generates feature maps for machine learning inputs.",{"name":82,"@type":73,"acceptedAnswer":83},"How is muscle stiffness information incorporated into the evaluation?",{"text":84,"@type":76},"The thesis compares the GLCM-based feature maps against elastography ultrasound results to explore correlations with muscle stiffness and to assess performance in the presence of MTrPs.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]