Acharya, U. Rajendra and Meiburger, Kristen M. and Wei Koh, Joel En and Ciaccio, Edward J. and Arunkumar, N. and See, Mee Hoong and Mohd Taib, Nur Aishah and Vijayananthan, Anushya and Rahmat, Kartini and Fadzli, Farhana and Leong, Sook Sam and Westerhout, Caroline Judy and Chantre-Astaiza, Angela and Ramirez-Gonzalez, Gustavo (2019) A Novel Algorithm for Breast Lesion Detection Using Textons and Local Configuration Pattern Features With Ultrasound Imagery. IEEE Access, 7. pp. 22829-22842. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2019.2898121.
Full text not available from this repository.Abstract
Breast cancer is the most commonly occurring cancer in women worldwide. While mammography remains the gold standard in breast cancer screening, ultrasound is an important imaging modality for both screening and cancer diagnosis. This paper presents a novel method for the detection of breast lesions in ultrasound images using texton filter banks, local configuration pattern features, and classification, without employing any segmentation technique. The developed method was able to accurately detect and classify breast lesions and achieved an accuracy, sensitivity, specificity, and positive predictive value of 96.1%, 96.5%, 95.3%, and 97.9%, respectively. The paradigm that we describe may, therefore, be useful as an effective tool to detect breast nodules during screening and in whole breast imaging, enabling clinicians to focus on images where a lesion is already known to be present. The developed method may also serve as a component for automatic breast nodule detection, and, when found, for the subsequent classification between lesion type benign versus malignant. © 2013 IEEE.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | benign; Breast; classifier; image; local configuration pattern; malignant; texton; ultrasound |
| Subjects: | R Medicine |
| Divisions: | Faculty of Medicine |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 18 May 2020 02:50 |
| Last Modified: | 18 May 2020 02:50 |
| URI: | http://eprints.um.edu.my/id/eprint/24296 |
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