A fuzzy clustering based color-coded diagram for effective illustration of blood perfusion parameters in contrast-enhanced ultrasound videos

Zhemin Zhuang; Guangwen Fan; Ye Yuan; Alex Noel Joseph Raj; Shunmin Qiu

doi:10.1016/j.cmpb.2019.105233

A fuzzy clustering based color-coded diagram for effective illustration of blood perfusion parameters in contrast-enhanced ultrasound videos

Comput Methods Programs Biomed. 2020 Jul:190:105233. doi: 10.1016/j.cmpb.2019.105233. Epub 2019 Nov 19.

Authors

Zhemin Zhuang¹, Guangwen Fan², Ye Yuan¹, Alex Noel Joseph Raj³, Shunmin Qiu⁴

Affiliations

¹ Department of Electronic Engineering, Shantou University, Shantou, Guangdong, China; Guangdong Provincial Key Laboratory of Digital Signal and Image Processing, Shantou University, Shantou, Guangdong, China.
² Department of Electronic Engineering, Shantou University, Shantou, Guangdong, China.
³ Department of Electronic Engineering, Shantou University, Shantou, Guangdong, China; Guangdong Provincial Key Laboratory of Digital Signal and Image Processing, Shantou University, Shantou, Guangdong, China. Electronic address: jalexnoel@stu.edu.cn.
⁴ Imaging Department, First Hospital of Medical College of Shantou University, Shantou, Guangdong, China.

PMID: 31796224
DOI: 10.1016/j.cmpb.2019.105233

Abstract

Background and objective: Early identification and diagnosis of tumors are of great significance to improve the survival rate of patients. Amongst other techniques, contrast-enhanced ultrasound is an important means to help doctors diagnose tumors. Due to the advantages of high efficiency, accuracy and objectivity, more and more computer-aided methods are used in medical diagnosis. Here we propose, a color-coded diagram based on quantitative blood perfusion parameters for contrast-enhanced ultrasound video. The method realizes the static description of the dynamic blood perfusion process in contrast-enhanced ultrasound videos and reveal the blood perfusion characteristics of all regions of the tissue providing assistance to the doctors in their clinical diagnosis.

Methods: For effective illustration of the blood perfusion through tissues, we propose (a) an improved block matching algorithm to eliminate the image distortions caused by breathing; (b) compute the time-grayscale intensity curve for each pixel to obtain four different quantitative blood perfusion parameters; and finally (c) employ the fuzzy C-means clustering algorithm to cluster the blood perfusion parameters, where each parameter is associated with a particular color. Thus based on the correspondence between the pixel and the blood perfusion parameters, all the pixels are color-coded to obtain the color-coded diagram.

Results: To the best of our knowledge, the proposed technique is one-of-its-kind to color code the contrast-enhanced ultrasound videos using blood perfusion parameters in order to understand the hemodynamic characteristics of the benign and malignant lesion. In our experiments, various contrast-enhanced ultrasound videos corresponding to several real-world cases were color-coded and the results of the experiments illustrated that the proposed color-coded diagrams are consistent with the diagnosis presented by the physicians.

Conclusions: The experimental results suggested that the proposed method can comprehensively describe the blood perfusion characteristics of tissues during the angiography process thereby effectively assisting the doctors in diagnosis.

Keywords: Blood perfusion parameters; Color-coded diagram; Fuzzy c-means clustering algorithm; Time-grayscale intensity curve; Video stabilization.

MeSH terms

Algorithms
Cluster Analysis
Color*
Contrast Media
Fuzzy Logic*
Humans
Image Interpretation, Computer-Assisted / methods*
Neoplasms / diagnostic imaging
Perfusion*
Ultrasonography*

Substances

Contrast Media