{"title":"利用新型 MOT-SF 技术进行 PCO 形态分割的创新方法","authors":"B. Poorani, Rashmita Khilar","doi":"10.1007/s10791-024-09458-1","DOIUrl":null,"url":null,"abstract":"<p>Polycystic ovary syndrome (PCOS) is an endocrine disorder affecting women of reproductive age characterized by the presence of multiple follicles in the ovaries that are detectable via ultrasound imaging. Early diagnosis of PCOS morphology can be challenging due to low resolution and increased speckle noise, making it difficult to identify smaller follicle boundaries. This article introduces a novel methodology, multiscale gradient-weighted oriented Otsu thresholding with sum of product fusion (MOT-SF), to address these challenges. The MOT-SF technique precisely recognizes smaller region boundaries even at lower resolutions by employing a pyramidal structure for image computation at multiple scales. Otsu's thresholding is used to segment the image, optimizing the threshold by minimizing the interclass variance at each stage. Incorporating gradient weights (λ) within classes enhances smaller boundary regions and reduces noise. Additionally, the MOT-SF method integrates a sum of product fusion strategies, combining segmented images from various scales to produce a final image that preserves both small and large PCOS structures while mitigating noise. The experimental results show that MOT-SF outperforms traditional methods such as Otsu’s thresholding and Chan-Vese models, as well as deep learning approaches such as R-CNN, in terms of computational efficiency and robustness to variations in ultrasound image quality. The MOT-SF methodology achieves an accuracy of nearly 85% and a precision of 94%, highlighting its potential to improve the detection and characterization of follicles in ultrasound images and advancing diagnostic tools in reproductive health.</p><h3 data-test=\"abstract-sub-heading\">Graphical Abstract</h3>\n","PeriodicalId":54352,"journal":{"name":"Information Retrieval Journal","volume":"35 1","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An innovative approach for PCO morphology segmentation using a novel MOT-SF technique\",\"authors\":\"B. Poorani, Rashmita Khilar\",\"doi\":\"10.1007/s10791-024-09458-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Polycystic ovary syndrome (PCOS) is an endocrine disorder affecting women of reproductive age characterized by the presence of multiple follicles in the ovaries that are detectable via ultrasound imaging. Early diagnosis of PCOS morphology can be challenging due to low resolution and increased speckle noise, making it difficult to identify smaller follicle boundaries. This article introduces a novel methodology, multiscale gradient-weighted oriented Otsu thresholding with sum of product fusion (MOT-SF), to address these challenges. The MOT-SF technique precisely recognizes smaller region boundaries even at lower resolutions by employing a pyramidal structure for image computation at multiple scales. Otsu's thresholding is used to segment the image, optimizing the threshold by minimizing the interclass variance at each stage. Incorporating gradient weights (λ) within classes enhances smaller boundary regions and reduces noise. Additionally, the MOT-SF method integrates a sum of product fusion strategies, combining segmented images from various scales to produce a final image that preserves both small and large PCOS structures while mitigating noise. The experimental results show that MOT-SF outperforms traditional methods such as Otsu’s thresholding and Chan-Vese models, as well as deep learning approaches such as R-CNN, in terms of computational efficiency and robustness to variations in ultrasound image quality. The MOT-SF methodology achieves an accuracy of nearly 85% and a precision of 94%, highlighting its potential to improve the detection and characterization of follicles in ultrasound images and advancing diagnostic tools in reproductive health.</p><h3 data-test=\\\"abstract-sub-heading\\\">Graphical Abstract</h3>\\n\",\"PeriodicalId\":54352,\"journal\":{\"name\":\"Information Retrieval Journal\",\"volume\":\"35 1\",\"pages\":\"\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Retrieval Journal\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s10791-024-09458-1\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Retrieval Journal","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10791-024-09458-1","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
An innovative approach for PCO morphology segmentation using a novel MOT-SF technique
Polycystic ovary syndrome (PCOS) is an endocrine disorder affecting women of reproductive age characterized by the presence of multiple follicles in the ovaries that are detectable via ultrasound imaging. Early diagnosis of PCOS morphology can be challenging due to low resolution and increased speckle noise, making it difficult to identify smaller follicle boundaries. This article introduces a novel methodology, multiscale gradient-weighted oriented Otsu thresholding with sum of product fusion (MOT-SF), to address these challenges. The MOT-SF technique precisely recognizes smaller region boundaries even at lower resolutions by employing a pyramidal structure for image computation at multiple scales. Otsu's thresholding is used to segment the image, optimizing the threshold by minimizing the interclass variance at each stage. Incorporating gradient weights (λ) within classes enhances smaller boundary regions and reduces noise. Additionally, the MOT-SF method integrates a sum of product fusion strategies, combining segmented images from various scales to produce a final image that preserves both small and large PCOS structures while mitigating noise. The experimental results show that MOT-SF outperforms traditional methods such as Otsu’s thresholding and Chan-Vese models, as well as deep learning approaches such as R-CNN, in terms of computational efficiency and robustness to variations in ultrasound image quality. The MOT-SF methodology achieves an accuracy of nearly 85% and a precision of 94%, highlighting its potential to improve the detection and characterization of follicles in ultrasound images and advancing diagnostic tools in reproductive health.
期刊介绍:
The journal provides an international forum for the publication of theory, algorithms, analysis and experiments across the broad area of information retrieval. Topics of interest include search, indexing, analysis, and evaluation for applications such as the web, social and streaming media, recommender systems, and text archives. This includes research on human factors in search, bridging artificial intelligence and information retrieval, and domain-specific search applications.