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

• Published in: Computer methods and programs in biomedicine Vol. 190; p. 105233
• Main Authors: Zhuang, Zhemin, Fan, Guangwen, Yuan, Ye, Joseph Raj, Alex Noel, Qiu, Shunmin
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.07.2020
• Subjects: Algorithms; Blood perfusion parameters; Cluster Analysis; Color; Color-coded diagram; Contrast Media; Fuzzy c-means clustering algorithm; Fuzzy Logic; Humans; Image Interpretation, Computer-Assisted - methods; Neoplasms - diagnostic imaging; Perfusion; Time-grayscale intensity curve; Ultrasonography; Video stabilization; Time-grayscale intensity curve; Color-coded diagram; Fuzzy c-means clustering algorithm; Video stabilization; Blood perfusion parameters
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2019.105233

•Color coded diagram can reveal the blood perfusion characteristics of the tissue.•The improved block matching method can effectively stabilize the ultrasound videos.•Fuzzy Clustering Method is used to construct color coded diagram.•The results of the color coded diagram are consistent with the confirmed diagnosis.•The proposed color coded diagram can be used to identify benign/malignant tumors. 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. 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. 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. 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. [Display omitted]