19 December 2017 Diffuse optical tomography using semiautomated coregistered ultrasound measurements
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Abstract
Diffuse optical tomography (DOT) has demonstrated huge potential in breast cancer diagnosis and treatment monitoring. DOT image reconstruction guided by ultrasound (US) improves the diffused light localization and lesion reconstruction accuracy. However, DOT reconstruction depends on tumor geometry provided by coregistered US. Experienced operators can manually measure these lesion parameters; however, training and measurement time are needed. The wide clinical use of this technique depends on its robustness and faster imaging reconstruction capability. This article introduces a semiautomated procedure that automatically extracts lesion information from US images and incorporates it into the optical reconstruction. An adaptive threshold-based image segmentation is used to obtain tumor boundaries. For some US images, posterior shadow can extend to the chest wall and make the detection of deeper lesion boundary difficult. This problem can be solved using a Hough transform. The proposed procedure was validated from data of 20 patients. Optical reconstruction results using the proposed procedure were compared with those reconstructed using extracted tumor information from an experienced user. Mean optical absorption obtained from manual measurement was 0.21±0.06  cm−1 for malignant and 0.12±0.06  cm−1 for benign cases, whereas for the proposed method it was 0.24±0.08  cm−1 and 0.12±0.05  cm−1, respectively.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Atahar Mostafa, Atahar Mostafa, Hamed Vavadi, Hamed Vavadi, K. M. Shihab Uddin, K. M. Shihab Uddin, Quing Zhu, Quing Zhu, } "Diffuse optical tomography using semiautomated coregistered ultrasound measurements," Journal of Biomedical Optics 22(12), 121610 (19 December 2017). https://doi.org/10.1117/1.JBO.22.12.121610 . Submission: Received: 7 May 2017; Accepted: 4 December 2017
Received: 7 May 2017; Accepted: 4 December 2017; Published: 19 December 2017
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