A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images

Ultrasound imaging has become one of the most popular medical imaging modalities with numerous diagnostic applications. However, ultrasound (US) image segmentation, which is the essential process for further analysis, is a challenging task due to the poor image quality. In this paper, we propose a new segmentation scheme to combine both region- and edge-based information into the robust graph-based (RGB) segmentation method. The only interaction required is to select two diagonal points to determine a region of interest (ROI) on the original image. The ROI image is smoothed by a bilateral filter and then contrast-enhanced by histogram equalization. Then, the enhanced image is filtered by pyramid mean shift to improve homogeneity. With the optimization of particle swarm optimization (PSO) algorithm, the RGB segmentation method is performed to segment the filtered image. The segmentation results of our method have been compared with the corresponding results obtained by three existing approaches, and four metrics have been used to measure the segmentation performance. The experimental results show that the method achieves the best overall performance and gets the lowest ARE (10.77%), the second highest TPVF (85.34%), and the second lowest FPVF (4.48%).

[1]  Ling Zhang,et al.  A Fully Automatic Image Segmentation Using an Extended Fuzzy Set , 2011 .

[2]  Nikhil R. Pal,et al.  Image thresholding: Some new techniques , 1993, Signal Process..

[3]  H. B. Kekre,et al.  Tumour Delineation using Statistical Properties of The Breast US Images and Vector Quantization based Clustering Algorithms , 2013 .

[4]  Tao Mei,et al.  Personalized Video Recommendation through Graph Propagation , 2014, TOMM.

[5]  Dar-Ren Chen,et al.  Watershed segmentation for breast tumor in 2-D sonography. , 2004, Ultrasound in medicine & biology.

[6]  P. Wells,et al.  Speckle in ultrasonic imaging , 1981 .

[7]  Ruey-Feng Chang,et al.  3-D snake for US in margin evaluation for malignant breast tumor excision using mammotome , 2003, IEEE Transactions on Information Technology in Biomedicine.

[8]  Liang Gao,et al.  Phase- and GVF-Based Level Set Segmentation of Ultrasonic Breast Tumors , 2012, J. Appl. Math..

[9]  Xianglong Tang,et al.  Probability density difference-based active contour for ultrasound image segmentation , 2010, Pattern Recognit..

[10]  Umi Kalthum Ngah,et al.  Automatic Detection of Breast Tumours from Ultrasound Images Using the Modified Seed Based Region Growing Technique , 2005, KES.

[11]  Bhabesh Deka,et al.  ULTRASOUND IMAGE SEGMENTATION USING WATERSHEDS AND REGION MERGING , 2006 .

[12]  Tianfu Wang,et al.  Semi-automatic Breast Ultrasound Image Segmentation Based on Mean Shift and Graph Cuts , 2014, Ultrasonic imaging.

[13]  Dorin Comaniciu,et al.  Mean shift analysis and applications , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[14]  Lei Zhu,et al.  Multiscale geodesic active contours for ultrasound image segmentation using speckle reducing anisotropic diffusion , 2014 .

[15]  Fang-Cheng Yeh,et al.  Cell-competition algorithm: a new segmentation algorithm for multiple objects with irregular boundaries in ultrasound images. , 2005, Ultrasound in medicine & biology.

[16]  Michael Brady,et al.  Segmentation of ultrasound B-mode images with intensity inhomogeneity correction , 2002, IEEE Transactions on Medical Imaging.

[17]  A. Stavros,et al.  Solid breast nodules: use of sonography to distinguish between benign and malignant lesions. , 1995, Radiology.

[18]  Xuelong Li,et al.  A level set method with shape priors by using locality preserving projections , 2015, Neurocomputing.

[19]  Wei Zhang,et al.  An Improved Approach for Accurate and Efficient Measurement of Common Carotid Artery Intima-Media Thickness in Ultrasound Images , 2014, BioMed research international.

[20]  M. Giger,et al.  Computerized lesion detection on breast ultrasound. , 2002, Medical physics.

[21]  Xuelong Li,et al.  High-Order Energies for Stereo Segmentation , 2016, IEEE Transactions on Cybernetics.

[22]  Michael Elad,et al.  On the origin of the bilateral filter and ways to improve it , 2002, IEEE Trans. Image Process..

[23]  H. D. Cheng,et al.  A novel segmentation method for breast ultrasound images based on neutrosophic l-means clustering. , 2012, Medical physics.

[24]  D. Boukerroui,et al.  Multiresolution texture based adaptive clustering algorithm for breast lesion segmentation. , 1998, European journal of ultrasound : official journal of the European Federation of Societies for Ultrasound in Medicine and Biology.

[25]  K. Han,et al.  Breast lesions on sonograms: computer-aided diagnosis with nearly setting-independent features and artificial neural networks. , 2003, Radiology.

[26]  R. Chang,et al.  Tumor detection in automated breast ultrasound images using quantitative tissue clustering. , 2014, Medical physics.

[27]  Qinghua Huang,et al.  A Novel Graph-Based Segmentation Method for Breast Ultrasound Images , 2016, 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA).

[28]  Ruey-Feng Chang,et al.  Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks. , 2002, Ultrasound in medicine & biology.

[29]  Dorin Comaniciu,et al.  Database-guided breast tumor detection and segmentation in 2D ultrasound images , 2010, Medical Imaging.

[30]  SpitzerHedva,et al.  Multi-scale texture-based level-set segmentation of breast B-mode images , 2016 .

[31]  Woo Kyung Moon,et al.  Segmentation of breast tumor in three-dimensional ultrasound images using three-dimensional discrete active contour model. , 2003, Ultrasound in medicine & biology.

[32]  R. Carlson,et al.  Breast Cancer in Limited‐Resource Countries: An Overview of the Breast Health Global Initiative 2005 Guidelines , 2006, The breast journal.

[33]  Ling Zhang,et al.  Automated breast cancer detection and classification using ultrasound images: A survey , 2015, Pattern Recognit..

[34]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[35]  Xiao Liu,et al.  Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset , 2016, Neurocomputing.

[36]  Lubomir M. Hadjiiski,et al.  Malignant and benign breast masses on 3D US volumetric images: effect of computer-aided diagnosis on radiologist accuracy. , 2007, Radiology.

[37]  Fei Xu,et al.  Neutro-Connectedness Cut , 2015, IEEE Transactions on Image Processing.

[38]  Lian-Wen Jin,et al.  A robust graph-based segmentation method for breast tumors in ultrasound images. , 2012, Ultrasonics.

[39]  Dimitris N. Metaxas,et al.  Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions , 2003, IEEE Transactions on Medical Imaging.

[40]  Jiawei Tian,et al.  A novel breast ultrasound image segmentation algorithm based on neutrosophic similarity score and level set , 2018, Comput. Methods Programs Biomed..

[41]  Xuelong Li,et al.  Graph-based learning for segmentation of 3D ultrasound images , 2015, Neurocomputing.

[42]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[43]  Dar-Ren Chen,et al.  Automatic Contouring for Breast Tumors in 2-D Sonography , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[44]  Hedva Spitzer,et al.  Multi-scale texture-based level-set segmentation of breast B-mode images , 2016, Comput. Biol. Medicine.

[45]  P. P. Bansod,et al.  Carotid Artery Segmentation in Ultrasound Images and Measurement of Intima-Media Thickness , 2013, BioMed research international.

[46]  Xuelong Li,et al.  GA-SIFT: A new scale invariant feature transform for multispectral image using geometric algebra , 2014, Inf. Sci..

[47]  M. N. Vrahatis,et al.  Particle swarm optimization method in multiobjective problems , 2002, SAC '02.

[48]  Woo Kyung Moon,et al.  Level Set Contouring for Breast Tumor in Sonography , 2007, Journal of Digital Imaging.

[49]  Xuelong Li,et al.  Selective Level Set Segmentation Using Fuzzy Region Competition , 2016, IEEE Access.

[50]  Xuelong Li,et al.  Optimized graph-based segmentation for ultrasound images , 2014, Neurocomputing.

[51]  J. Alison Noble,et al.  Ultrasound image segmentation: a survey , 2006, IEEE Transactions on Medical Imaging.

[52]  C. Lamberti,et al.  Maximum likelihood segmentation of ultrasound images with Rayleigh distribution , 2005, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[53]  Rozi Mahmud,et al.  Segmentation of masses from breast ultrasound images using parametric active contour algorithm , 2010 .

[54]  Hee Chan Kim,et al.  Computer-aided diagnosis of solid breast nodules on ultrasound with digital image processing and artificial neural network , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[55]  Chunming Li,et al.  Distance Regularized Level Set Evolution and Its Application to Image Segmentation , 2010, IEEE Transactions on Image Processing.

[56]  Maryellen L Giger,et al.  Performance of computer-aided diagnosis in the interpretation of lesions on breast sonography. , 2004, Academic radiology.

[57]  Dar-Ren Chen,et al.  Breast cancer diagnosis using image retrieval for different ultrasonic systems , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[58]  Xuelong Li,et al.  Automatic segmentation of breast lesions for interaction in ultrasonic computer-aided diagnosis , 2015, Inf. Sci..

[59]  Vicent Caselles,et al.  Texture-Oriented Anisotropic Filtering and Geodesic Active Contours in Breast Tumor Ultrasound Segmentation , 2007, Journal of Mathematical Imaging and Vision.

[60]  W. C. A. Pereira,et al.  Feature selection and classifier performance in computer-aided diagnosis for breast ultrasound , 2013, 2013 10th International Conference and Expo on Emerging Technologies for a Smarter World (CEWIT).

[61]  Eran A. Edirisinghe,et al.  Fully automatic lesion boundary detection in ultrasound breast images , 2007, SPIE Medical Imaging.