Segmentation of ultrasound breast images based on a neutrosophic method

Breast cancer is one of the leading cancers of women. Ultrasound is often used for breast cancer diagnosis because it is harmless, portable, and low-cost. However, the segmentation of breast ultrasound (BUS) images is a difficult task due to their low contrast and speckle noise. Neutrosophy studies the origin, nature, and scope of neutralities and their interactions with different ideational spectra. It is a new philosophy to extend fuzzy logic and is the basis of neutrosophic logic, neutrosophic probability theory, neutrosophic set theory, and neutrosophic statistics. In this paper, we employ neutrosophy and develop a fully automatic algorithm for BUS image segmentation. By using neutrosophy, we integrate two conflicting opinions about speckle in ultrasound image: speckle is noise and speckle includes pattern information. The experiments demonstrate that the proposed approach is accurate, effective, and robust.

[1]  Robert M. Kirberger,et al.  IMAGING ARTIFACTS IN DIAGNOSTIC ULTRASOUND—A REVIEW , 1995 .

[2]  Yung-Sheng Chen,et al.  A disk expansion segmentation method for ultrasonic breast lesions , 2009, Pattern Recognit..

[3]  Heng-Da Cheng,et al.  Color image segmentation based on homogram thresholding and region merging , 2002, Pattern Recognit..

[4]  A. Jemal,et al.  Cancer Statistics, 2008 , 2008, CA: a cancer journal for clinicians.

[5]  Olivier Basset,et al.  Segmentation of ultrasound images--multiresolution 2D and 3D algorithm based on global and local statistics , 2003, Pattern Recognit. Lett..

[6]  Xianglong Tang,et al.  Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images , 2010, Pattern Recognit..

[7]  Ming Zhang,et al.  A high performance edge detector based on fuzzy inference rules , 2007, Inf. Sci..

[8]  Heng-Da Cheng,et al.  A NEW NEUTROSOPHIC APPROACH TO IMAGE THRESHOLDING , 2008 .

[9]  Rafayah Mousa,et al.  Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural , 2005, Expert Syst. Appl..

[10]  Hui Zhang,et al.  Image segmentation evaluation: A survey of unsupervised methods , 2008, Comput. Vis. Image Underst..

[11]  Max Mignotte,et al.  Endocardial Boundary E timation and Tracking in Echocardiographic Images using Deformable Template and Markov Random Fields , 2001, Pattern Analysis & Applications.

[12]  Yingtao Zhang,et al.  A novel approach to speckle reduction in ultrasound imaging. , 2009, Ultrasound in medicine & biology.

[13]  Sankar K. Pal,et al.  Fuzzy Mathematical Approach to Pattern Recognition , 1986 .

[14]  Yongmin Kim,et al.  A methodology for evaluation of boundary detection algorithms on medical images , 1997, IEEE Transactions on Medical Imaging.

[15]  Heng-Da Cheng,et al.  Microcalcification detection using fuzzy logic and scale space approaches , 2004, Pattern Recognit..

[16]  H. D. Cheng,et al.  Automatically Determine the Membership Function Based on the Maximum Entropy Principle , 1997, Inf. Sci..

[17]  James F. Greenleaf,et al.  Segmenting high-frequency intracardiac ultrasound images of myocardium into infarcted, ischemic, and normal regions , 2001, IEEE Transactions on Medical Imaging.

[18]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.

[19]  Rachid Deriche,et al.  Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

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

[21]  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.

[22]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[23]  R. Chang,et al.  Improvement in breast tumor discrimination by support vector machines and speckle-emphasis texture analysis. , 2003, Ultrasound in medicine & biology.

[24]  Johan Montagnat,et al.  Anisotropic filtering for model-based segmentation of 4D cylindrical echocardiographic images , 2003, Pattern Recognit. Lett..

[25]  Heng-Da Cheng,et al.  Computer-aided detection and classification of microcalcifications in mammograms: a survey , 2003, Pattern Recognit..

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

[27]  Heng-Da Cheng,et al.  A novel fuzzy logic approach to contrast enhancement , 2000, Pattern Recognit..

[28]  William M. Wells,et al.  Simultaneous validation of image segmentation and assessment of expert quality [tumor MRI application] , 2002, Proceedings IEEE International Symposium on Biomedical Imaging.

[29]  Heng-Da Cheng,et al.  Approaches for automated detection and classification of masses in mammograms , 2006, Pattern Recognit..

[30]  B. Goldberg,et al.  Ultrasound as a complement to mammography and breast examination to characterize breast masses. , 2002, Ultrasound in Medicine and Biology.

[31]  Kevin J. Parker,et al.  Multiple Resolution Bayesian Segmentation of Ultrasound Images , 1995 .

[32]  Milan Sonka,et al.  Segmentation and interpretation of MR brain images. An improved active shape model , 1998, IEEE Transactions on Medical Imaging.

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

[34]  Ruey-Feng Chang,et al.  3-D breast ultrasound segmentation using active contour model. , 2003, Ultrasound in medicine & biology.

[35]  Sung Hyun Kim,et al.  Correlation of ultrasound findings with histology, tumor grade, and biological markers in breast cancer , 2008, Acta oncologica.

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

[37]  Florentin Smarandache,et al.  A unifying field in logics : neutrosophic logic : neutrosophy, neutrosophic set, neutrosophic probability , 2020 .