Modeling of Hidden Markov in Ultrasound Image-Assisted Diagnosis

Shulan Li,Jinrong Ni,Hongmei Wu,Liping Shao,Zubang Zhou

doi:10.1155/2021/5597591

Abstract

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.

Highlights

At present, there are more and more patients with clinical pulmonary nodules. e 1–30 mm lung nodules found by X-ray or chest CT account for 0.2% or 1%, respectively
Segmentation Process of Lung Nodule Images. e process of segmentation of lung parenchyma is mainly divided into three steps: initial contour segmentation of the lung, lung contour repair, and lung parenchymal extraction. e overall process of the algorithm is shown in Figure 1 [10]
Aiming at the problem that the convex hull algorithm is used to repair the crossover effect of the internal contour of the lung parenchyma, this paper uses the improved vector method to repair

Summary

Introduction

There are more and more patients with clinical pulmonary nodules. e 1–30 mm lung nodules found by X-ray or chest CT account for 0.2% or 1%, respectively. There are more and more patients with clinical pulmonary nodules. E 1–30 mm lung nodules found by X-ray or chest CT account for 0.2% or 1%, respectively. Most of them are benign nodules, and malignant knots account for 20%. The nature of pulmonary nodules is determined by chest CT-guided percutaneous lung biopsy, bronchoscopy, or even open lung biopsy to confirm the pathology. Ese invasive procedures have the disadvantages of high risk and uneconomical and even bring unnecessary harm to patients with benign pulmonary nodules. Since the 1990s, lung nodule detection based on CT images has gradually become an important research content in the field of computer-aided diagnosis. Many universities and research institutions at home and abroad have carried out a lot of experimental work on the construction of lung nodule detection models

Methods

Results

Discussion

Conclusion