Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
Journal of Imaging Informatics in Medicine
Published in: Journal of Digital Imaging 3/2019

01-06-2019 | Magnetic Resonance Imaging

Levels Propagation Approach to Image Segmentation: Application to Breast MR Images

Authors: Fatah Bouchebbah, Hachem Slimani

Published in: Journal of Imaging Informatics in Medicine | Issue 3/2019

Login to get access

Abstract

Accurate segmentation of a breast tumor region is fundamental for treatment. Magnetic resonance imaging (MRI) is a widely used diagnostic tool. In this paper, a new semi-automatic segmentation approach for MRI breast tumor segmentation called Levels Propagation Approach (LPA) is introduced. The introduced segmentation approach takes inspiration from tumor propagation and relies on a finite set of nested and non-overlapped levels. LPA has several features: it is highly suitable to parallelization and offers a simple and dynamic possibility to automate the threshold selection. Furthermore, it allows stopping of the segmentation at any desired limit. Particularly, it allows to avoid to reach the breast skin-line region which is known as a significant issue that reduces the precision and the effectiveness of the breast tumor segmentation. The proposed approach have been tested on two clinical datasets, namely RIDER breast tumor dataset and CMH-LIMED breast tumor dataset. The experimental evaluations have shown that LPA has produced competitive results to some state-of-the-art methods and has acceptable computation complexity.
Literature
1.
Hecht F, Pessoa CF, Gentile LB, Rosenthal D, Carvalho DP, Fortunato RS: The role of oxidative stress on breast cancer development and therapy. Tumor Biol 37: 4281–4291, 2016CrossRef
2.
3.
Banaie M, Soltanian-Zadeh H, Saligheh-Rad HR, Gity M: Spatiotemporal features of DCE-MRI for breast cancer diagnosis. Comput Methods Programs Biomed 155: 153–164, 2018CrossRefPubMed
4.
Ferreira A, Gentil F, Tavares JMR: Segmentation algorithms for ear image data towards biomechanical studies. Comput Methods Biomech Biomed Engin 17: 888–904, 2014CrossRefPubMed
5.
Mughal B, Sharif M: Automated detection of breast tumor in different imaging modalities: A Review. Current Medical Imaging Reviews 13: 121–139, 2017CrossRef
6.
Scoggins M, Dogan B, Ma J, Wei W, Song JB, Candelaria RP, Litton JK, Arun B: Short breast MRI screening trial in women at high-risk for breast cancer. J Clin Oncol 35: e13049, 2017CrossRef
7.
Ma Z, Tavares JMR, Jorge RN, Mascarenhas T: A review of algorithms for medical image segmentation and their applications to the female pelvic cavity. Comput Methods Biomech Biomed Engin 13: 235–246, 2010CrossRefPubMed
8.
Singh SP, Urooj S: An improved CAD system for breast cancer diagnosis based on generalized pseudo-Zernike moment and Ada-DEWNN classifier. J Med Syst 40: 105–118, 2016CrossRefPubMed
9.
Jida S, Aksasse B, Ouanan M (2017) Face segmentation and detection using Voronoi diagram and 2D histogram. In: Intelligent systems and computer vision, Fez, 1-5
10.
Oliveira RB, Mercedes Filho E, Ma Z, Papa JP, Pereira AS, Tavares JMR: Computational methods for the image segmentation of pigmented skin lesions: a review. Comput Methods Programs Biomed 131: 127–141, 2016CrossRefPubMed
11.
Shi J, Sahiner B, Chan HP, Paramagul C, Hadjiiski LM, Helvie M, Chenevert T: Treatment response assessment of breast masses on dynamic contrast-enhanced magnetic resonance scans using fuzzy c-means clustering and level set segmentation. Med Phys 36: 5052–5063, 2009CrossRefPubMedPubMedCentral
12.
Yin D, Lu RW (2015) A method of breast tumour MRI segmentation and 3D reconstruction. In: 7th International conference on information technology in medicine and education, Huangshan, 23-26
13.
Liu H, Liu Y, Zhao Z, Zhang L, Qiu T: A new background distribution-based active contour model for three-dimensional lesion segmentation in breast DCE-MRI. Med Phys 41(8), Part 1, 2014
14.
Agner SC, Xu J, Madabhushi A: Spectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging. Med Phys 40(3), 2013
15.
Al-Faris AQ, Ngah UK, Isa NAM, Shuaib IL: Computer-aided segmentation system for breast MRI tumour using modified automatic seeded region growing (BMRI-MASRG). J Digit Imaging 27: 133–144, 2014CrossRefPubMed
16.
Cui Y, Tan Y, Zhao B, Liberman L, Parbhu R, Kaplan J, Theodoulou M, Hudis C, Schwartz LH: Malignant lesion segmentation in contrast-enhanced breast MR images based on the marker-controlled watershed. Med Phys 36: 4359–4369, 2009CrossRefPubMedPubMedCentral
17.
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 Sensors 1: 138–148, 2011
18.
Fix E, Hodges JL (1951) Discriminatory analysis, nonparametric discrimination: Consistency properties, Technical Report 4, USAF School of Aviation Medicine, Randolph fiels TX
19.
Yao J, Chen J, Chow C: Breast tumor analysis in dynamic contrast enhanced MRI using texture features and wavelet transform. IEEE J Sel Top Sign Proces 3: 94–100, 2009CrossRef
20.
Wu Q, Salganicoff M, Krishnan A, Fussell DS, Markey MK (2006) Interactive lesion segmentation on dynamic contrast enhanced breast MRI using a markov model. In: Proc. SPIE medical imaging: Image processing, 6144, San Diego
21.
Chen W, Giger ML, Bick U: A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR Images. Acad Radiol 13: 63–72, 2006CrossRefPubMed
22.
Moftah HM, Azar AT, Al-Shammari ET, Ghali NI, Hassanien AE, Shoman M: Adaptive K-means clustering algorithm for MR breast image segmentation. Neural Comput Appl 24: 1917–1928, 2014CrossRef
23.
Hassanien AE, Moftah HM, Azar A T, Shoman M: MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier. Appl Soft Comput 14: 62–71, 2014CrossRef
24.
McClymont D, Mehnert A, Trakic A, Kennedy D, Crozier S: Fully automatic lesion segmentation in breast MRI using mean-shift and graph-cuts on a region adjacency graph. J Magn Reson Imaging 39: 795–804, 2014CrossRefPubMed
25.
Yu N, Wu J, Weinstein SP, Gaonkar B, Keller BM, Ashraf AB, Jiang Y, Davatzikos C, Conant EF, Kontos D (2015) A superpixel-based framework for automatic tumor segmentation on breast DCE-MRI. In Proc. SPIE medical imaging: Computer-aided diagnosis, 9414, Orlando
26.
Jayender J, Chikarmane S, Jolesz FA, Gombos E: Automatic segmentation of invasive breast carcinomas from dynamic contrast-enhaced MRI using time series analysis. J Magn Reson Imaging 40: 467–475, 2014CrossRefPubMed
27.
Maicas G, Carneiro G, Bradley AP (2017) Globally optimal breast mass segmentation from DCE-MRI using deep semantic segmentation as shape prior. In: 14th international symposium on biomedical imaging, Melbourne, 305-309
28.
Zaheeruddin Z, Jaffery ZA, Singh L (2012) Detection and shape feature extraction of breast tumor in mammograms. In: Proceedings of the world congress on engineering, London, pp 719–724
29.
Xie X, Yeo SY, Mirmehdi M, Sazonov I, Nithiarasu P: Image gradient based level set methods in 2D and 3D. In: (González HM, et al, Eds.) Deformation Models, Netherlands, 2013, pp 101–120
30.
Pavlidis G (2017) Segmentation of digital images. In: Mixed Raster Content. Signals and communication technology, Singapore, 213-260
31.
32.
33.
Scott RE, Wille JJ, Wier ML: Mechanisms for the initiation and promotion of carcinogenesis: a review and a new concept. Mayo Clin Proc 59: 107–117, 1984CrossRefPubMed
34.
Adams R, Bischof L: Seeded region growing. IEEE Trans Pattern Anal Mach Intell 16: 641–647, 1994CrossRef
35.
Malek AA, Rahman WEZWA, Haris MHM, Jalil UMA: Segmenting masses in ultrasound images by using seed based region growing and mathematical morphology. Adv Sci Lett 23: 11512–11516, 2017CrossRef
36.
Shrivastava A, Chaudhary A, Kulshreshtha D, Singh VP, Srivastava R (2017) Automated digital mammogram segmentation using dispersed region growing and sliding window algorithm. In: 2nd international conference on image, vision and computing, Chengdu, pp 366–370
37.
Solves LJ, Monserrat C, Rupérez MJ, Naranjo V, Alajami M, Feliu F, Garcìa M, Lloret M: MRI skin segmentation for the virtual deformation of the breast under mammographic compression. Stud Health Technol Inform 173: 483–489, 2012
38.
Al-Faris AQ, Isa NAM, Ngah UK, Shuaib IL (2015) Automatic exclusion of skin border regions from breast MRI using proposed combined approach. In: 2nd international conference on biomedical engineering, Perlis, pp 1–6
39.
40.
Li C, Huang R, Ding Z, Gatenby J, Metaxas DN, Gore JC: A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans Image Process 20: 2007–2016, 2011CrossRefPubMed
Metadata
Title
Levels Propagation Approach to Image Segmentation: Application to Breast MR Images
Authors
Fatah Bouchebbah
Hachem Slimani
Publication date
01-06-2019
Publisher
Springer International Publishing
Published in
Journal of Imaging Informatics in Medicine / Issue 3/2019
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI
https://doi.org/10.1007/s10278-018-00171-2

Other articles of this Issue 3/2019

Journal of Digital Imaging 3/2019 Go to the issue

OriginalPaper

Utilization of Structured Reporting to Monitor Outcomes of Doppler Ultrasound Performed for Deep Vein Thrombosis

OriginalPaper

Automatic Nasopharyngeal Carcinoma Segmentation Using Fully Convolutional Networks with Auxiliary Paths on Dual-Modality PET-CT Images

OriginalPaper

Multi-objective Parameter Auto-tuning for Tissue Image Segmentation Workflows

OriginalPaper

Evaluating Completeness of a Radiology Glossary Using Iterative Refinement

OriginalPaper

The Role of the Integrated Digital Radiology System in Assessing the Impact of Patient Load on Emergency Computed Tomography (CT) Efficiency

OriginalPaper

Probabilistic Modeling of Exam Durations in Radiology Procedures