of 39 Vector Field Analysis for Segmentation of the Ultrasound Images of Breast Cancer Stanislav S. Makhanov School of Information and Computer Technology, Sirindhorn International Institute of Technology, Thammasat UniversityThailand

of 39 Biomedical Engineering Unit, Sirindhorn International Institute of Technology Prof. Dr. Stanislav Makhanov,Unit Head, medical image processing, CNC machining of medical implants Assoc. Prof. Dr. Bunyarit Uyyanonvara medical image processing, retinal image analysis Assoc. Prof. Dr. Waree Kongprawechnon, control engineering Assoc. Prof. Dr. Siriwan Suebnukarn, Department of Dentistry, TU, dentistry, virtual reality Dr. Itthisek Nilkhamhang control engineering, robotics Assoc. Prof. Dr. Utairat Chaumrattanakul, Department of Radiology: ultrasound diagnostics Asst. Prof. Toshiaki Kondo medical image processing Asst. Prof. Dr. Pakinee Aimmanee retinal image processing

of 39 “Breast cancer is the chief cause of natural death among women in Thailand. In 2012 alone, Thailand saw 13,184 new cases, leading to 4,665 deaths. Bluntly put, there is roughly one death from breast cancer every two hours” (The Nation, October,2013) Stage5-year Survival Rate 0100% I II93% III72% IV22% Breast cancer survival rates. The US National Cancer Institute’s Database Breast cancer

of 39 Ultrasound imaging for breast cancer diagnostics One of the most practical and rapid method of obtaining breast cancer images Relatively inexpensive technology with the benefits of portability Non invasive, real-time and free of harmful radiation

of 39 Input: the US image Output: the contour of the tumor Segmentation of Ultrasound Images

of 39 Snakes (active contours) Active contours or snakes are closed evolving curves designed for segmentation of complex shaped 2D objects object

of 39 resists stretching resists bending (corners) The external force moves the snake towards the object. The force is based on the image features such as edges elasticitystiffness 7 The curve is treated as an elastic rubber band characterized by elasticity and stiffness Mathematical Model

of 39 Snake Based Segmentation

of 39 attracts the snake to the intensity I desired attracts the snake to large gradients of the image gray level attracts the snake to the edges External force

of 39 Some typical sources of the errors

of 39 Small capture range Gradient vector field Edge map Gradient field Small capture range

of 39 Noise, shadows false boundaries ground truth snake gets distracted by the noise Cancer tumor in an ultrasound image Noise

of 39 Inappropriate parameters It is often hard to find appropriate parameters of the snake ?? Wrong parameters

of 39 The snake evolves too fast or too slow. It stops at the false boundary or does not stop at all. Slow convergence It is hard to define an appropriate convergence criterion. When enough is enough ? Breast cancer segmentation from an ultrasound image No convergence

of 39 Poor initialization Inappropriate seed

of 39 Concave objects Wrong result

of 39 Snake can not automatically split into several snakes to detect multiple objects Multiple objects Wrong result

of 39 Embedded objects Snakes can not automatically detect the structure of embedded objects Wrong result

of 39 Gradient Vector Flow: Mathematical model Diffusion, smooth and extends the vector field Stopping term Gradient vector flow field Conventional Improved diffusion

of 39 Are able to repel or attract each other, split, merge, and disappear as necessary. They are also capable of producing offspring anti-snakes. Multiple Cooperating High Order Snakes The snake points repel each other when they are close enough Contracting snake Expanding snake The two snakes capture the object

of 39 Multiple snakes

of 39 Vector field analysis

of 39 Phase portrait analysis Noise or boundary noise background boundary

of 39 Force Field Analysis: Direction score Rotating window Measuring the deviation from anti-parallel positions

of 39 Direction score

of 39 Direction score as an edge detector The best conventional edge detector Direction score

of 39 Gradient Vector Flow with the Direction Score

of 39 The edge magnitude score OriginalPreprocessedEdge map Clustering Magnitude score

of 39 Gradient Vector Flow with the edge magnitude

of 39 Multi-feature diffusion orientation (force field analysis) Magnitude (clustering) Weak edge Strong edge Weak edge shadows no edge

of 39 Reference methods Classical GGVF with manual training Criterion/ Method No preprocessing GGVF + best K 0.09 Preprocessing GGVF manualMulti-feature vector flow Best K=0.13 Acceptable range: K= TP H1H H2H H3H Snake convergen ce yes GGVF convergen ce explicit scheme no yes TP – true positives, H 1 - max Hausdorff distance, H 2 - average Hausdorff distance, H 3 relative to the contour length Hausdorff distance x 1000

of 39 Reference methods Poisson gradient vector flow C.Y. Hsu, C.Y. Liu, C.M. Chen, Automatic segmentation of liver PET images, Computerized Medical Imaging and Graphics, vol. 32, 2008, pp. 601–610. C.Y. Hsu, K.F. Wang, H.C. Wang, K.K. Tseng, Automatic extraction of face contours in images and videos, Future Generation Computer Systems, vol 28, 2012, pp. 322–335 (M2012) Mixed noise vector flow O. Ghita, P. F.Whelan, A new GVF-based image enhancement formulation for use in the presence of mixed noise, Pattern Recognition, vol 43, 2010, pp. 2646–2658 (M2010) B. Li, S.T. Acton (2007) Active contour external force using vector field convolution for image Segmentation, IEEE Transactions on Inage Processing, vol 16(8), pp (M2007) Convolution vector flow J. Cheng, S.W. Foo, Dynamic directional gradient vector flow for snakes, IEEE Transactions on Image Processing, vol 15( 6), 2006, pp Directional gradient vector flow

of 39 Testing on synthetic tumors

of 39 Testing against recent methods on synthetic tumors Noise/ Criterion/Method M2010 M2012 M2007 Multi-feature diffusion PSNR=24db % images, TP>80% 100% % 99.83% % 99.81% % 99.89% Average TP Average H 1 Average H 2 PSNR=17db % images, TP>80% 52.63% 85.71% % 88.99% % 92.24% % 96.17% Average TP Average H 1 Average H 2 PSNR=15db % images, TP>80% NA NA 52.63% 86.23% % 91.83% Average TP Average H 1 Average H 2 # of training parameters none

of 39 Testing recent methods on the US image database onlinemedicalimages.com

of 39 Multi feature diffusion vs. recent methods Criterion/Method M2010 M2012 M2007 Multi-feature diffusion % images, TP>80% 64% % 89.86% % 89.07% % 92.06% Average TP Average H 1 Average H 2 # of training parameters none

of 39 Multi-feature vs. the standard methods Canny edge detection Direction score Magnitude score

of 39 Multi-feature vs. the standard methods Original Ground truth M2011 M2012M2007 Multi-feature

of 39 Other Applications

of 39 Original imageExtraction results Extracting roads from satellite Images

of 39 Defeating Digital CAPTCHAS 78% recognition rate

of 39 Thank you