B-Mode Ultrasound Breast Anatomy Segmentation

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

João Teixeira (Author) FEUP; Carreiro, AM (Author) Other; Santos, RM (Author) Other; Oliveira, HP (Author) FCUP

Conference proceedings International

Title: Image Analysis and Recognition - 17th International Conference, ICIAR 2020, Póvoa de Varzim, Portugal, June 24-26, 2020, Proceedings, Part II

Pages: 193-201

17th International Conference on Image Analysis and Recognition, ICIAR 2020

24 June 2020 through 26 June 2020

Indexing

Scopus - 3 Citations

Other information

Authenticus ID: P-00S-B28

DOI: 10.1007/978-3-030-50516-5_17

Abstract (EN): Breast Ultrasound has long been used to support diagnostic and exploratory procedures concerning breast cancer, with an interesting success rate, specially when complemented with other radiology information. This usability can further enhance visualization tasks during pre-treatment clinical analysis by coupling the B-Mode images to 3D space, as found in Magnetic Resonance Imaging (MRI) per instance. In fact, Lesions in B-mode are visible and present high detail when comparing with other 3D sequences. This coupling, however, would be largely benefited from the ability to match the various structures present in the B-Mode, apart from the broadly studied lesion. In this work we focus on structures such as skin, subcutaneous fat, mammary gland and thoracic region. We provide a preliminary insight to several structure segmentation approaches in the hopes of obtaining a functional and dependable pipeline for delineating these potential reference regions that will assist in multi-modal radiological data alignment. For this, we experiment with pre-processing stages that include Anisotropic Diffusion guided by Log-Gabor filters (ADLG) and main segmentation steps using K-Means, Meanshift and Watershed. Among the pipeline configurations tested, the best results were found using the ADLG filter that ran for 50 iterations and H-Maxima suppression of 20% and the K-Means method with $$K=6$$. The results present several cases that closely approach the ground truth despite overall having larger average errors. This encourages the experimentation of other approaches that could withstand the innate data variability that makes this task very challenging. © Springer Nature Switzerland AG 2020.

Language: English

Type (Professor's evaluation): Scientific