Authors: Ahlem Melouah; Soumaya Layachi
Addresses: LRI Laboratory, Department of Informatics, Badji-Mokhtar Annaba University, P.O. Box 12, 23000, Annaba, Algeria ' LRI Laboratory, Department of Informatics, Badji-Mokhtar Annaba University, P.O. Box 12, 23000, Annaba, Algeria
Abstract: The success of mass detection using seeded region growing segmentation depends on seed point selection operation. The seed point is the first point from which starts the process of aggregation. This point must be inside the mass otherwise the segmentation fails. There are two principal ways to perform the seed point selection. The first one is manual, performed by a medical expert who manually outlines the point of interest using a pointer device. The second one is automatic; in this case the whole process is performed without any user interaction. This paper proposes a novel approach to select automatically the seed point for further region growing expansion. Firstly, suspicious regions are extracted by a thresholding technique. Secondly, the suspicious region whose features match with the predefined mass features is identified as the region of interest. Finally, the seed point is placed inside the region of interest. The proposed method is tested using the IRMA database and the MIAS database. The experimental results show the performance and robustness of the proposed method.
Keywords: breast cancer; masses detection; mammograms; segmentation; seeded region growing; automatic seed selection; region of interest; ROI; features; thresholding.
International Journal of Computational Science and Engineering, 2019 Vol.18 No.1, pp.80 - 88
Received: 28 Dec 2015
Accepted: 24 Jul 2016
Published online: 14 Dec 2018 *