Reserach On Key Technologies Of Benign And Malignant Classification Based On Dual-mode Breast Ultrasound Images

Breast cancer is one of the most common cancers and affects women’s healthseriously. Early detection is an effective way to control the disease. Breastultrasound imaging has become one of the most prevalent and popular approachesfor breast cancer diagnosis due to the fact that it is radiation-free, non-invasive,painless, cost-effective and portable. In order to improve the effectiveness andaccuracy, computer-aided diagnostic (CAD) techniques are more and more appliedto clinical practice. Existing classification methods usually extract the geometricfeatures, boundary features and texture features from a single frame captured fromB-Mode ultrasound video. However, due to body position changes, physiologicalchanges, complexity and diversity of ultrasound imaging, cross and overlap withbenign and malignant tumors, analyzing the image with the features extracted fromthe single frame will affect the diagnostic accuracy inevitably. In addition, singlemode image can not provide enough clinic information; therefore, it is necessary tofuse the image with different model to achieve a comprehensive and synthesizedanalysis.To overcome the one-sidedness of single mode image, this dissertationproposed to integrate the features of B-Mode ultrasound image and color Dopplerultrasound image sequence to analyze the benign and malignant breast tumorsynthetically. Wherein, image segmentation, image registration and describing thedynamic information of image sequence are the key problems of feature extractionfrom static image and dynamic image sequence which have been well studied.However, there are still problems in processing of breast ultrasound images.According to the different image characteristic, this dissertation proposed severalmethods according to a series of conventional processes including imagesegmentation, image registration, feature extraction and classification to solve theproblems above to the breast ultrasound images.The main research work and contribution of this dissertation are:(1) A novel breast ultrasound (BUS) image segmentation algorithm based oncellular automata is studied. Due to high noise, complicate structure and blurryboundary, breast ultrasound image segmentation is a difficult task. To overcome the problems, an energy decrease strategy is used for modeling the spatial relationinformation of pixels according to the energy transition principle of cellularautomata. Then, a seed information comparison function and a texture informationcomparison function are proposed for modeling the global image informationdifference and local image information difference. In addition, two neighborhoodsystems (von Neumann and Moore neighborhood systems) are integrated as theevolution environment, and a similarity-based criterion is used for suppressing noiseand reducing computation complexity. The proposed method is helpful to handleBUS image with blurry boundaries and low contrast well, segment BUS imageaccurately and effectively within a simple initial condition.(2) A fully automatic non-rigid image registration algorithm based on opticalflow principle is studied for registration of BUS images. To overcome the affectionof speckle noise, complicate structure of BUS image, this dissertation proposed toapply the inertia principle to the image registration, and an “inertia force” derivedfrom the local motion trend of pixels in a Moore neighborhood system is producedand integrated into optical flow equation by the Newton’s second law to estimate theacceleration direction, which is helpful to handle the speckle noise and preserve thegeometric continuity of image. In addition, the proposed method integrated the ideaof Newton’s second law to accelerate the convergence speed. The proposed methodis helpful to register ultrasound images efficiently, robust to noise, quickly andautomatically.(3) A breast tumor classification method based on B-Mode ultrasound imageand color Doppler image sequence is studied. First, the color moment and colorentropy methods are utilized for modeling the vascularity features. For extractinghemodynamic features, an image registration method is utilized for mapping theposition of corresponding blood signals in different phases. Then the color Dopplerimage is divided into non-overlapping lattices. From each lattice, a discrete Dopplerwaveform is constructed from the registration results and several importanthemodynamic features are extracted. Furthermore, a velocity coherence vectormethod is proposed to design to the region of interest for extracting the localhemodynamic features. Finally, these features are employed to discriminate benignmasses from malignant masses by using the support vector machine classifier. Theproposed method is helpful to improve the true-positive and decrease the false-positive diagnostic rate, which is useful for reducing the unnecessary biopsyand death rate.