Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (4): 916-924.doi: 10.13229/j.cnki.jdxbgxb20200896

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Panoramic mosaic approach of ultrasound medical images based on template fusion

Hai-yang JIA1,2(),Rui XIA1,An-qi LYU1,Ceng-xuan GUAN1,Juan CHEN1,2(),Lei WANG3   

  1. 1.College of Computer Science and technology，Jilin University，Changchun 130012，China
    2.Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education，Jilin University，Changchun 130012，China
    3.The First Hospital of Jilin University，Changchun 130012，China
  • Received:2020-11-23 Online:2022-04-01 Published:2022-04-20
  • Contact: Juan CHEN E-mail:jiahy@jlu.edu.cn;chenjuan@jlu.edu.cn

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Abstract:

The panoramic mosaic technology can get a larger field of observation view， based on the partial medical images obtained by ultrasonic detection device. Through stitching and fusing， it can provide more intuitive and comprehensive information for clinical diagnosis and doctor-patient communication. However， due to the influence of objective factors such as low precision image and more noise caused by medical equipment， the results of traditional panoramic mosaic method are not ideal. To solve the problems above， a template fusion method based on improved k-MEANS clustering algorithm for ultrasonic medical image mosaic is proposed. Compared with the traditional template stitching algorithm， this method can self-organize the region with dense features into flexible template， and improve the speed of adjacent image matching by using heuristic window algorithm. The experimental results show that the template detection rate and average template density are improved， the image registration accuracy is improved， and the method has high robustness and real-time performance.

Key words: ultrasound image, fusion splicing, image processing, template matching

CLC Number: 

  • TP391.4

Fig.1

Flow chart of ultrasound image mosaic"

Fig.2

Schematic diagram of Sobel operator"

Fig.3

Extraction effect of image feature points"

Fig.4

Structure presentation of KD tree based on image"

Fig.5

Data structure presentation of KD tree"

Fig.6

Comparison of two adjacent frames obtained by k-MEANS clustering"

Fig.7

Fusion effect of image growth technology"

Fig.8

Fusion effect of mean"

Fig.9

Weighted fusion effect"

Fig.10

Experiment results of set 1"

Fig.11

Experiment results of set 2"

Fig.12

Mosaic image of a complete blood vessel obtained by proposed method"

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