Skip to main content
Erschienen in: Journal of Digital Imaging 1/2014

01.02.2014

Computer-Aided Segmentation System for Breast MRI Tumour using Modified Automatic Seeded Region Growing (BMRI-MASRG)

verfasst von: Ali Qusay Al-Faris, Umi Kalthum Ngah, Nor Ashidi Mat Isa, Ibrahim Lutfi Shuaib

Erschienen in: Journal of Imaging Informatics in Medicine | Ausgabe 1/2014

Einloggen, um Zugang zu erhalten

Abstract

In this paper, an automatic computer-aided detection system for breast magnetic resonance imaging (MRI) tumour segmentation will be presented. The study is focused on tumour segmentation using the modified automatic seeded region growing algorithm with a variation of the automated initial seed and threshold selection methodologies. Prior to that, some pre-processing methodologies are involved. Breast skin is detected and deleted using the integration of two algorithms, namely the level set active contour and morphological thinning. The system is applied and tested on 40 test images from the RIDER breast MRI dataset, the results are evaluated and presented in comparison to the ground truths of the dataset. The analysis of variance (ANOVA) test shows that there is a statistically significance in the performance compared to the previous segmentation approaches that have been tested on the same dataset where ANOVA p values for the evaluation measures’ results are less than 0.05, such as: relative overlap (p = 0.0002), misclassification rate (p = 0.045), true negative fraction (p = 0.0001) and sum of true volume fraction (p = 0.0001).
Literatur
1.
Zurück zum Zitat Gardiner I: CAD improves breast MRI workflow: increasing throughput while maintaining accuracy in breast MRI reads requires powerful workflow tools, 2010 Gardiner I: CAD improves breast MRI workflow: increasing throughput while maintaining accuracy in breast MRI reads requires powerful workflow tools, 2010
2.
Zurück zum Zitat Lehman CD, Peacock S, DeMartini WB, Chen X: A new automated software system to evaluate breast MR examinations: improved specificity without decreased sensitivity. Am J Roentgenol 187:51–56, 2006CrossRef Lehman CD, Peacock S, DeMartini WB, Chen X: A new automated software system to evaluate breast MR examinations: improved specificity without decreased sensitivity. Am J Roentgenol 187:51–56, 2006CrossRef
3.
Zurück zum Zitat Li L, Clark RA, Thomas JA: Computer-aided diagnosis of masses with full-field digital mammography. Acad Radiol 9:4–12, 2002PubMedCrossRef Li L, Clark RA, Thomas JA: Computer-aided diagnosis of masses with full-field digital mammography. Acad Radiol 9:4–12, 2002PubMedCrossRef
4.
Zurück zum Zitat Verma B, Zakos J: A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques. IEEE Trans Inf Technol Biomed 5:46–54, 2001PubMedCrossRef Verma B, Zakos J: A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques. IEEE Trans Inf Technol Biomed 5:46–54, 2001PubMedCrossRef
5.
Zurück zum Zitat Ibrahim S, Khalid NEA, Manaf M: Empirical study of brain segmentation using particle swarm optimization. Shah Alam, Selangor, 2010 Ibrahim S, Khalid NEA, Manaf M: Empirical study of brain segmentation using particle swarm optimization. Shah Alam, Selangor, 2010
6.
Zurück zum Zitat Ganesan R, Radhakrishnan S: Segmentation of computed tomography brain images using genetic algorithm. Int J Soft Comput 4:157–161, 2009 Ganesan R, Radhakrishnan S: Segmentation of computed tomography brain images using genetic algorithm. Int J Soft Comput 4:157–161, 2009
7.
Zurück zum Zitat Hussain R, Arif S, Sikander MA, Memon AR: Fuzzy clustering based malignant areas detection in noisy breast magnetic resonant (MR) images. Int J Acad Res 3(2):64, 2011 Hussain R, Arif S, Sikander MA, Memon AR: Fuzzy clustering based malignant areas detection in noisy breast magnetic resonant (MR) images. Int J Acad Res 3(2):64, 2011
8.
Zurück zum Zitat Kannan S, Sathya A, Ramathilagam S: Effective fuzzy clustering techniques for segmentation of breast MRI. Soft computing—a fusion of foundations. Methodol Appl 15:483–491, 2011 Kannan S, Sathya A, Ramathilagam S: Effective fuzzy clustering techniques for segmentation of breast MRI. Soft computing—a fusion of foundations. Methodol Appl 15:483–491, 2011
9.
Zurück zum Zitat Noor NM, Khalid NEA, Hassan R, Ibrahim S, Yassin IM: Adaptive Neuro-Fuzzy Inference System for Brain Abnormality Segmentation, 2010 Noor NM, Khalid NEA, Hassan R, Ibrahim S, Yassin IM: Adaptive Neuro-Fuzzy Inference System for Brain Abnormality Segmentation, 2010
10.
Zurück zum Zitat Azmi R, Anbiaee R, Norozi N, Salehi L, Amirzadi A: IMPST: a new interactive self-training approach to segmentation suspicious lesions in breast MRI. J Med Signals Sens 1:138–148, 2011PubMedCentralPubMed Azmi R, Anbiaee R, Norozi N, Salehi L, Amirzadi A: IMPST: a new interactive self-training approach to segmentation suspicious lesions in breast MRI. J Med Signals Sens 1:138–148, 2011PubMedCentralPubMed
11.
Zurück zum Zitat Adams R, Bischof L: Seeded region growing. IEEE Trans Pattern Anal Machine Intell 16:641–647, 1994 Adams R, Bischof L: Seeded region growing. IEEE Trans Pattern Anal Machine Intell 16:641–647, 1994
12.
Zurück zum Zitat Khalid NEA, Ibrahim S, Manaf M, Ngah UK: Seed-Based Region Growing Study for Brain Abnormalities Segmentation. Proc. Information Technology (ITSim), 2010 International Symposium: City, 15–17 June 2010 Year Khalid NEA, Ibrahim S, Manaf M, Ngah UK: Seed-Based Region Growing Study for Brain Abnormalities Segmentation. Proc. Information Technology (ITSim), 2010 International Symposium: City, 15–17 June 2010 Year
13.
Zurück zum Zitat Ibrahim S, Khalid NEA, Manaf M, Ngah UK: Particle swarm optimization vs seed-based region growing: brain abnormalities segmentation. Int J Artif Intell 7:174–188, 2011 Ibrahim S, Khalid NEA, Manaf M, Ngah UK: Particle swarm optimization vs seed-based region growing: brain abnormalities segmentation. Int J Artif Intell 7:174–188, 2011
14.
Zurück zum Zitat Meinel LA: Development of computer-aided diagnostic system for breast MRI lesion classification. Dissertation. University of Iowa, Biomedical Engineering, PhD (Doctor of Philosophy), dissertation 2005 Meinel LA: Development of computer-aided diagnostic system for breast MRI lesion classification. Dissertation. University of Iowa, Biomedical Engineering, PhD (Doctor of Philosophy), dissertation 2005
15.
Zurück zum Zitat Mat-Isa NA, Mashor MY, Othman NH: Seeded region growing features extraction algorithm; its potential use in improving screening for cervical cancer. Int J Comput Internet Manag 13(1):61–70, 2005 Mat-Isa NA, Mashor MY, Othman NH: Seeded region growing features extraction algorithm; its potential use in improving screening for cervical cancer. Int J Comput Internet Manag 13(1):61–70, 2005
16.
Zurück zum Zitat Wu J, Poehlman S, Noseworthy MD, Kamath MV: Texture feature based automated seeded region growing in abdominal MRI segmentation. Proc. BioMedical Engineering and Informatics BMEI 2008: City, 27–30 May 2008 Year Wu J, Poehlman S, Noseworthy MD, Kamath MV: Texture feature based automated seeded region growing in abdominal MRI segmentation. Proc. BioMedical Engineering and Informatics BMEI 2008: City, 27–30 May 2008 Year
17.
Zurück zum Zitat Shan J, Cheng HD, Wang Y: A novel automatic seed point selection algorithm for breast ultrasound images. Proc. Pattern Recognition, 2008 ICPR 2008 19th International Conference: City, 8–11 Dec. 2008 Year Shan J, Cheng HD, Wang Y: A novel automatic seed point selection algorithm for breast ultrasound images. Proc. Pattern Recognition, 2008 ICPR 2008 19th International Conference: City, 8–11 Dec. 2008 Year
18.
Zurück zum Zitat Chun-yu N, Shu-fen L, Ming Q: Research on Removing Noise in Medical Image Based on Median Filter Method, 2009 Chun-yu N, Shu-fen L, Ming Q: Research on Removing Noise in Medical Image Based on Median Filter Method, 2009
19.
Zurück zum Zitat Li C, Xu C, Gui C, Fox MD: Level set evolution without re-initialization: a new variational formulation. Proc. Proceedings of the International Conference on Computer Vision and Pattern Recognition: City Li C, Xu C, Gui C, Fox MD: Level set evolution without re-initialization: a new variational formulation. Proc. Proceedings of the International Conference on Computer Vision and Pattern Recognition: City
20.
Zurück zum Zitat Al-Faris AQ, Ngah UK, Isa NAM, Shuaib IL: MRI breast skin-line segmentation and removal using integration method of level set active contour and morphological thinning algorithms. Journal of Medical Sciences, 2013 Al-Faris AQ, Ngah UK, Isa NAM, Shuaib IL: MRI breast skin-line segmentation and removal using integration method of level set active contour and morphological thinning algorithms. Journal of Medical Sciences, 2013
21.
Zurück zum Zitat Lam L, Seong-Whan L, Suen CY: Thinning methodologies—a comprehensive survey. IEEE Trans Pattern Anal Mach Intell 14:879, 1992 Lam L, Seong-Whan L, Suen CY: Thinning methodologies—a comprehensive survey. IEEE Trans Pattern Anal Mach Intell 14:879, 1992
22.
Zurück zum Zitat Chalana V, Kim Y: A methodology for evaluation of boundary detection algorithms on medical images. IEEE Trans Med Imaging 16:642–652, 1997PubMedCrossRef Chalana V, Kim Y: A methodology for evaluation of boundary detection algorithms on medical images. IEEE Trans Med Imaging 16:642–652, 1997PubMedCrossRef
23.
Zurück zum Zitat Fenster A, Chiu B: Evaluation of segmentation algorithms for medical imaging. Proc. Proceedings of IEEE 27th Annual Conference of the Engineering in Medicine and Biology Society: City, September 1–4 Year Fenster A, Chiu B: Evaluation of segmentation algorithms for medical imaging. Proc. Proceedings of IEEE 27th Annual Conference of the Engineering in Medicine and Biology Society: City, September 1–4 Year
25.
Zurück zum Zitat McNeil BJ, Hanley JA: Statistical approaches to analysis of receiver operating characteristic (ROC) curves. Med Decis Making 14:137–150, 1984CrossRef McNeil BJ, Hanley JA: Statistical approaches to analysis of receiver operating characteristic (ROC) curves. Med Decis Making 14:137–150, 1984CrossRef
26.
Zurück zum Zitat Song T, et al: A hybrid tissue segmentation approach for brain MR images. Med Biol Eng Comput 44:242, 2006PubMedCrossRef Song T, et al: A hybrid tissue segmentation approach for brain MR images. Med Biol Eng Comput 44:242, 2006PubMedCrossRef
27.
Zurück zum Zitat Ertas G, Gülçür HO, Osman O, Osman NU, Tunaci M, Dursun M: Breast MR segmentation and lesion detection with cellular neural networks and 3D template matching. Comput Biol Med 38(10):116–126, 2008PubMedCrossRef Ertas G, Gülçür HO, Osman O, Osman NU, Tunaci M, Dursun M: Breast MR segmentation and lesion detection with cellular neural networks and 3D template matching. Comput Biol Med 38(10):116–126, 2008PubMedCrossRef
28.
Zurück zum Zitat US National Cancer Institute: reference image database to evaluate therapy response (RIDER) MRI breast, 2007 US National Cancer Institute: reference image database to evaluate therapy response (RIDER) MRI breast, 2007
Metadaten
Titel
Computer-Aided Segmentation System for Breast MRI Tumour using Modified Automatic Seeded Region Growing (BMRI-MASRG)
verfasst von
Ali Qusay Al-Faris
Umi Kalthum Ngah
Nor Ashidi Mat Isa
Ibrahim Lutfi Shuaib
Publikationsdatum
01.02.2014
Verlag
Springer US
Erschienen in
Journal of Imaging Informatics in Medicine / Ausgabe 1/2014
Print ISSN: 2948-2925
Elektronische ISSN: 2948-2933
DOI
https://doi.org/10.1007/s10278-013-9640-5

Weitere Artikel der Ausgabe 1/2014

Journal of Digital Imaging 1/2014 Zur Ausgabe

Update Radiologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.