A novel automatic segmentation workflow of axial breast DCE-MRI

Feten Besbes; Norhene Gargouri; Alima Damak; Dorra Sellami

doi:10.1117/12.2309698

13 April 2018 A novel automatic segmentation workflow of axial breast DCE-MRI

Feten Besbes, Norhene Gargouri, Alima Damak, Dorra Sellami

Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 106960Q (2018) https://doi.org/10.1117/12.2309698
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria

Abstract

In this paper we propose a novel process of a fully automatic breast tissue segmentation which is independent from expert calibration and contrast. The proposed algorithm is composed by two major steps. The first step consists in the detection of breast boundaries. It is based on image content analysis and Moore-Neighbour tracing algorithm. As a processing step, Otsu thresholding and neighbors algorithm are applied. Then, the external area of breast is removed to get an approximated breast region. The second preprocessing step is the delineation of the chest wall which is considered as the lowest cost path linking three key points; These points are located automatically at the breast. They are respectively, the left and right boundary points and the middle upper point placed at the sternum region using statistical method. For the minimum cost path search problem, we resolve it through Dijkstra algorithm. Evaluation results reveal the robustness of our process face to different breast densities, complex forms and challenging cases. In fact, the mean overlap between manual segmentation and automatic segmentation through our method is 96.5%. A comparative study shows that our proposed process is competitive and faster than existing methods. The segmentation of 120 slices with our method is achieved at least in 20.57±5.2s.

Citation Download Citation

Feten Besbes, Norhene Gargouri, Alima Damak, and Dorra Sellami "A novel automatic segmentation workflow of axial breast DCE-MRI", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106960Q (13 April 2018); https://doi.org/10.1117/12.2309698

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