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Automatic Extraction of Blood Flow Area in Brachial Artery for Suspicious Hypertension Patients from Color Doppler Sonography with Fuzzy C-Means Clustering

  • Kim, Kwang Baek (Division of Computer Software Engineering, Silla University) ;
  • Song, Doo Heon (Department of Computer Games, Yong-In Songdam College) ;
  • Yun, Sang-Seok (Division of Mechanical Convergence Engineering, Silla University)
  • Received : 2018.10.05
  • Accepted : 2018.12.06
  • Published : 2018.12.31

Abstract

Color Doppler sonography is a useful tool for examining blood flow and related indices. However, it should be done by well-trained operator, that is, operator subjectivity exists. In this paper, we propose an automatic blood flow area extraction method from brachial artery that would be an essential building block of computer aided color Doppler analyzer. Specifically, our concern is to examine hypertension suspicious (prehypertension) patients who might develop their symptoms to established hypertension in the future. The proposed method uses fuzzy C-means clustering as quantization engine with careful seeding of the number of clusters from histogram analysis. The experiment verifies that the proposed method is feasible in that the successful extraction rates are 96% (successful in 48 out of 50 test cases) and demonstrated better performance than K-means based method in specificity and sensitivity analysis but the proposed method should be further refined as the retrospective analysis pointed out.

Keywords

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Fig. 1. Preprocessing steps: (a) input sonography, (b) binarized, (c) focused on ROI, and (d) after noise removal.

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Fig. 2. Preparation for FCM based quantization. (a) Picks from histogram analysis and (b) Typical membership function for FCM quantization.

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Fig. 3. Effect of FCM quantization. (a) Quantized by FCM and (b) Extracted by human (red).

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Fig. 4. Examples of successful extractions. (a) Input and (b) Successful extraction.

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Fig. 5. Different blood flow area extractions by FCM and K-means: (a) Original input, (b) Extraction by FCM, and (c) Extraction by K-means.

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Fig. 6. Failed extractions. (a) Case 1 and (b) Case 2.

Acknowledgement

Grant : IoT based Assistive Robot Systems for Personalized Healthcare

Supported by : National Research Foundation of Korea (NRF), Ministry of Trade, Industry & Energy (MOTIE)

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