Modeling of Hidden Markov in Ultrasound Image-Assisted Diagnosis

Different segmentation of lung nodules using the same segmentation algorithm can easily lead to excessive segmentation errors. Therefore, it is necessary to design an effective segmentation algorithm to improve image segmentation accuracy. Based on the hidden Markov model, this study processed the u...

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Main Authors: Liping Shao, Zubang Zhou, Hongmei Wu, Jinrong Ni, Shulan Li
Format: Article
Language:English
Published: Hindawi Limited 2021-01-01
Series:Journal of Healthcare Engineering
Online Access:http://dx.doi.org/10.1155/2021/5597591
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spelling doaj-e6a9eeef766d43c693a2038f5c4309852021-04-26T00:04:08ZengHindawi LimitedJournal of Healthcare Engineering2040-23092021-01-01202110.1155/2021/5597591Modeling of Hidden Markov in Ultrasound Image-Assisted DiagnosisLiping Shao0Zubang Zhou1Hongmei Wu2Jinrong Ni3Shulan Li4Department of UltrasoundDepartment of UltrasoundDepartment of UltrasoundDepartment of Cardiac SurgeryDepartment of UltrasoundDifferent segmentation of lung nodules using the same segmentation algorithm can easily lead to excessive segmentation errors. Therefore, it is necessary to design an effective segmentation algorithm to improve image segmentation accuracy. Based on the hidden Markov model, this study processed the ultrasound images of pulmonary nodules to improve their diagnostic results. At the same time, this study was combined with the ultrasound image of lung nodules to process the ultrasound images. In addition, this study combines the convex hull algorithm for image processing, uses the improved vector method to repair, improves image recognizability, establishes a reliable feature extraction algorithm, and establishes a comprehensive diagnostic model. Finally, this study designed the test for performance analysis. Through experimental research, it can be seen that the model constructed in this study has certain clinical effects and can provide theoretical reference for subsequent related research.http://dx.doi.org/10.1155/2021/5597591
collection DOAJ
language English
format Article
sources DOAJ
author Liping Shao
Zubang Zhou
Hongmei Wu
Jinrong Ni
Shulan Li
spellingShingle Liping Shao
Zubang Zhou
Hongmei Wu
Jinrong Ni
Shulan Li
Modeling of Hidden Markov in Ultrasound Image-Assisted Diagnosis
Journal of Healthcare Engineering
author_facet Liping Shao
Zubang Zhou
Hongmei Wu
Jinrong Ni
Shulan Li
author_sort Liping Shao
title Modeling of Hidden Markov in Ultrasound Image-Assisted Diagnosis
title_short Modeling of Hidden Markov in Ultrasound Image-Assisted Diagnosis
title_full Modeling of Hidden Markov in Ultrasound Image-Assisted Diagnosis
title_fullStr Modeling of Hidden Markov in Ultrasound Image-Assisted Diagnosis
title_full_unstemmed Modeling of Hidden Markov in Ultrasound Image-Assisted Diagnosis
title_sort modeling of hidden markov in ultrasound image-assisted diagnosis
publisher Hindawi Limited
series Journal of Healthcare Engineering
issn 2040-2309
publishDate 2021-01-01
description Different segmentation of lung nodules using the same segmentation algorithm can easily lead to excessive segmentation errors. Therefore, it is necessary to design an effective segmentation algorithm to improve image segmentation accuracy. Based on the hidden Markov model, this study processed the ultrasound images of pulmonary nodules to improve their diagnostic results. At the same time, this study was combined with the ultrasound image of lung nodules to process the ultrasound images. In addition, this study combines the convex hull algorithm for image processing, uses the improved vector method to repair, improves image recognizability, establishes a reliable feature extraction algorithm, and establishes a comprehensive diagnostic model. Finally, this study designed the test for performance analysis. Through experimental research, it can be seen that the model constructed in this study has certain clinical effects and can provide theoretical reference for subsequent related research.
url http://dx.doi.org/10.1155/2021/5597591
work_keys_str_mv AT lipingshao modelingofhiddenmarkovinultrasoundimageassisteddiagnosis
AT zubangzhou modelingofhiddenmarkovinultrasoundimageassisteddiagnosis
AT hongmeiwu modelingofhiddenmarkovinultrasoundimageassisteddiagnosis
AT jinrongni modelingofhiddenmarkovinultrasoundimageassisteddiagnosis
AT shulanli modelingofhiddenmarkovinultrasoundimageassisteddiagnosis
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