Classification of twinkling artifacts and blood flow for in vivo detection of breast microcalcifications.
Authors
Affiliations (6)
Affiliations (6)
- Department of Biomedical Software Engineering, The Catholic University of Korea, Bucheon 14662, South Korea. Electronic address: [email protected].
- Department of Computer Science, Whiting School of Engineering, the Johns Hopkins University, Baltimore, MD 21218, USA.
- Department of Electronic Engineering, Sogang University, Seoul 04107, South Korea.
- Department of Health & Medical Equipment, Samsung Electronics Co., Ltd., Suwon 16678, South Korea.
- Department of Radiology, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul 03722, South Korea; Department of Integrative Medicine Major in Digital Healthcare, Yonsei University College of Medicine, Seoul 06273, South Korea.
- Department of Electronic Engineering, Sogang University, Seoul 04107, South Korea; Department of Biomedical Engineering, Sogang University, Seoul 04107, South Korea.
Abstract
While mammography is the standard modality for detecting microcalcifications (MCs), their real-time detection with ultrasound imaging can be invaluable, particularly for guiding biopsies. Ultrasound twinkling artifact (TA) imaging allows the sensitive distinction of MCs from background breast tissue; however, it may also be confounded with blood flow in Doppler mode during in vivo scanning. In this paper, we propose a new MC imaging method that classifies TA and blood flow signals to enable in vivo detection of breast MCs. Based on the signal characteristics of TA and blood flow, two optimal features (i.e., mean frequency and spectrum bandwidth) are extracted and used to train a machine learning classifier. To train the classification model, tissue-mimicking and chicken breast phantom containing normal wire (285 μm in diameter), MC wire (300 μm in diameter) and micro-vessel tube (1 mm in diameter) were fabricated, and training and validation datasets were acquired under varying flow velocities and pulse repetition frequencies (PRFs). Among the four classifiers, i.e., k-nearest neighbors (KNN), support vector machine (SVM), naïve Bayes and quadratic discriminant, trained with the two optimal features, the SVM achieved the highest accuracy (95.25 %), whereas the remaining models also exhibited strong performance with accuracies exceeding 92 %. The trained SVM model was then validated on a chicken breast MC phantom and in vivo human breast data, and they showed good agreement with color Doppler imaging. The feasibility study demonstrated that the proposed classification approach may enable effective in vivo detection and improve diagnostic accuracy, especially in cases with complex flow patterns in breast lesions.