Automated Analysis of Ultrasound Videos for Detection of Breast Lesions
Message:
Abstract:
Background

Breast cancer is the second cause of death among women. Ultrasound (US) imaging is the most common technique for diagnosing breast cancer; however, detecting breast lesions in US images is a difficult task, mainly, because it provides low-quality images. Consequently, identifying lesions in US images is still a challenging task and an open problem in US image processing. This study aims to develop an automated system for the identification of lesions in US images

Method

We proposed an automatic method to assist radiologists in inspecting and analyzing US images in breast screening and diagnosing breast cancer. In contrast to previous research, this work focuses on fusing information extracted from different frames. The developed method consists of template matching, morphological features extraction, local binary patterns, fuzzy C-means clustering, region growing, and information fusion-based image segmentation technique. The performance of the system was evaluated using a database composed of 22 US videos where 10 breast US films were obtained from patients with breast lesions and 12 videos belonged to normal cases.

Results

The sensitivity, specificity, and accuracy of the system in detecting frames with breast lesions were 95.7%, 97.1%, and 97.1%, respectively. The algorithm reduced the vibration of the physician’s hands’ while probing by assessing every 10 frames regardless of the results of the prior frame; hence, lowering the possibility of missing a lesion during an examination.

Conclusion

The presented system outperforms several existing methods in correctly detecting breast lesions in a breast cancer screening test. Fusing information that exists in frames of a breast US film can help improve the identification of lesions (suspect regions) in a screening test.

Article Type:
Research/Original Article
Language:
English
Published:
Middle East Journal of Cancer, Volume:11 Issue:1, 2020
Pages:
80 - 90
magiran.com/p2067300  
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