Automatic multi-seed detection for MR breast image segmentation
Authors: Comelli, A., Bruno, A., Di Vittorio, M.L., Ienzi, F., Lagalla, R., Vitabile, S. and Ardizzone, E.
Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 10484 LNCS
Pages: 706-717
eISSN: 1611-3349
ISBN: 9783319685595
ISSN: 0302-9743
DOI: 10.1007/978-3-319-68560-1_63
Abstract:In this paper an automatic multi-seed detection method for magnetic resonance (MR) breast image segmentation is presented. The proposed method consists of three steps: (1) pre-processing step to locate three regions of interest (axillary and sternal regions); (2) processing step to detect maximum concavity points for each region of interest; (3) breast image segmentation step. Traditional manual segmentation methods require radiological expertise and they usually are very tiring and time-consuming. The approach is fast because the multi-seed detection is based on geometric properties of the ROI. When the maximum concavity points of the breast regions have been detected, region growing and morphological transforms complete the segmentation of breast MR image. In order to create a Gold Standard for method effectiveness and comparison, a dataset composed of 18 patients is selected, accordingly to three expert radiologists of University of Palermo Policlinico Hospital (UPPH). Each patient has been manually segmented. The proposed method shows very encouraging results in terms of statistical metrics (Sensitivity: 95.22%; Specificity: 80.36%; Precision: 98.05%; Accuracy: 97.76%; Overlap: 77.01%) and execution time (4.23 s for each slice).
https://eprints.bournemouth.ac.uk/34241/
Source: Scopus
Automatic Multi-seed Detection for MR Breast Image Segmentation
Authors: Comelli, A., Bruno, A., Di Vittorio, M.L., Ienzi, F., Lagalla, R., Vitabile, S. and Ardizzone, E.
Journal: IMAGE ANALYSIS AND PROCESSING,(ICIAP 2017), PT I
Volume: 10484
Pages: 706-717
eISSN: 1611-3349
ISBN: 978-3-319-68559-5
ISSN: 0302-9743
DOI: 10.1007/978-3-319-68560-1_63
https://eprints.bournemouth.ac.uk/34241/
Source: Web of Science (Lite)
Automatic multi-seed detection for MR breast image segmentation
Authors: Comelli, A., Bruno, A., Di Vittorio, M.L., Ienzi, F., Lagalla, R., Vitabile, S. and Ardizzone, E.
Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 10484 LNCS
Pages: 706-717
DOI: 10.1007/978-3-319-68560-1_63
https://eprints.bournemouth.ac.uk/34241/
Source: Manual
Automatic multi-seed detection for MR breast image segmentation
Authors: Comelli, A., Bruno, A., Di Vittorio, M.L., Ienzi, F., Lagalla, R., Vitabile, S. and Ardizzone, E.
Conference: 19th International Conference on Image Analysis and Processing
Pages: 706-717
Publisher: Springer
ISBN: 9783319685595
Abstract:In this paper an automatic multi-seed detection method for magnetic resonance (MR) breast image segmentation is presented. The proposed method consists of three steps: (1) pre-processing step to locate three regions of interest (axillary and sternal regions); (2) processing step to detect maximum concavity points for each region of interest; (3) breast image segmentation step. Traditional manual segmentation methods require radiological expertise and they usually are very tiring and time-consuming. The approach is fast because the multi-seed detection is based on geometric properties of the ROI. When the maximum concavity points of the breast regions have been detected, region growing and morphological transforms complete the segmentation of breast MR image. In order to create a Gold Standard for method effectiveness and comparison, a dataset composed of 18 patients is selected, accordingly to three expert radiologists of University of Palermo Policlinico Hospital (UPPH). Each patient has been manually segmented. The proposed method shows very encouraging results in terms of statistical metrics (Sensitivity: 95.22%; Specificity: 80.36%; Precision: 98.05%; Accuracy: 97.76%; Overlap: 77.01%) and execution time (4.23 s for each slice).
https://eprints.bournemouth.ac.uk/34241/
Source: BURO EPrints