Breast Lesion Segmentation in DCE- MRI Imaging

Breast cancer is one of the most common cancers in women. Typically, the course of the disease is asymptomatic in the early stages of breast cancer. Imaging breast examinations allow early detection of the cancer, which is associated with increased chances of a complete cure. There are many breast imaging techniques such as: mammography (MM), ultrasound imaging (US), positron-emission tomography (PET), computed tomography (CT), and magnetic resonance imaging (MRI). These imaging techniques differ in terms of effectiveness, price, type of physical phenomenon, the impact on the patient and its availability. In this paper, we focus on MRI imaging and we compare three breast lesion segmentation algorithms that have been tested on QIN Breast DCE-MRI database, which is publicly available. The obtained values of Dice and Jaccard indices indicate the segmentation using k-means algorithm.

[1]  George Hamer,et al.  Automated Single and Multi-Breast Tumor Segmentation Using Improved Watershed Technique in 2D MRI Images , 2016, RACS.

[2]  June-Goo Lee,et al.  Deep Learning in Medical Imaging: General Overview , 2017, Korean journal of radiology.

[3]  Christo Gnonnou,et al.  Segmentation and 3D reconstruction of MRI images for breast cancer detection , 2014, International Image Processing, Applications and Systems Conference.

[4]  T. Sørensen,et al.  A method of establishing group of equal amplitude in plant sociobiology based on similarity of species content and its application to analyses of the vegetation on Danish commons , 1948 .

[5]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  R. Devi,et al.  Robust kernel FCM in segmentation of breast medical images , 2011, Expert Syst. Appl..

[7]  Dinggang Shen,et al.  Segmentation and Classification of Breast Tumor Using Dynamic Contrast-Enhanced MR Images , 2007, MICCAI.

[8]  M. Giger,et al.  A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images. , 2006, Academic radiology.

[9]  P. Jaccard THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1 , 1912 .

[10]  Kayvan Najarian,et al.  Breast cancer detection in gadolinium‐enhanced MR images by static region descriptors and neural networks , 2003, Journal of magnetic resonance imaging : JMRI.

[11]  Stephen M. Moore,et al.  The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository , 2013, Journal of Digital Imaging.

[12]  Marta Danch-Wierzchowska,et al.  Simplification of breast deformation modelling to support breast cancer treatment planning , 2016 .

[13]  Aboul Ella Hassanien,et al.  Adaptive k-means clustering algorithm for MR breast image segmentation , 2013, Neural Computing and Applications.

[14]  Li Li,et al.  Computerized Segmentation and Characterization of Breast Lesions in Dynamic Contrast-Enhanced MR Images Using Fuzzy c-Means Clustering and Snake Algorithm , 2012, Comput. Math. Methods Medicine.

[15]  Umi Kalthum Ngah,et al.  Computer-Aided Segmentation System for Breast MRI Tumour using Modified Automatic Seeded Region Growing (BMRI-MASRG) , 2014, Journal of Digital Imaging.

[16]  Antonella Petrillo,et al.  Selection of Suspicious ROIs in Breast DCE-MRI , 2011, ICIAP.

[17]  L. Schwartz,et al.  Malignant lesion segmentation in contrast-enhanced breast MR images based on the marker-controlled watershed. , 2009, Medical physics.

[18]  Bilwaj Gaonkar,et al.  A superpixel-based framework for automatic tumor segmentation on breast DCE-MRI , 2015, Medical Imaging.

[19]  Maryellen L. Giger,et al.  A Fuzzy C-Means (FCM)-Based Approach for Computerized Segmentation of Breast Lesions in Dynamic Contrast-Enhanced MR Images1 , 2006 .

[20]  Hui Liu,et al.  A new active contour model-based segmentation approach for accurate extraction of the lesion from breast DCE-MRI , 2013, 2013 IEEE International Conference on Image Processing.

[21]  Daniel Rueckert,et al.  Patch-Based Evaluation of Image Segmentation , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  M. Shetty,et al.  Breast Cancer Screening and Diagnosis , 2015 .

[23]  L. R. Dice Measures of the Amount of Ecologic Association Between Species , 1945 .

[24]  Jayaram K. Udupa,et al.  A framework for evaluating image segmentation algorithms , 2006, Comput. Medical Imaging Graph..

[25]  Umi Kalthum Ngah,et al.  Breast MRI Tumour Segmentation Using Modified Automatic Seeded Region Growing Based on Particle Swarm Optimization Image Clustering , 2014 .

[26]  Nabil Wasif,et al.  MRI versus Ultrasonography and Mammography for Preoperative Assessment of Breast Cancer , 2009, The American surgeon.

[27]  Ron Kikinis,et al.  Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge. , 2014, Translational oncology.

[28]  S. R. Kannan,et al.  Effective fuzzy clustering techniques for segmentation of breast MRI , 2011, Soft Comput..

[29]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[31]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[32]  Xianghua Xie,et al.  MAC: Magnetostatic Active Contour Model , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Stuart Crozier,et al.  Fully automatic lesion segmentation in breast MRI using mean‐shift and graph‐cuts on a region adjacency graph , 2014, Journal of magnetic resonance imaging : JMRI.