Molybdenum Target Image Based On The Key Technology In Computer Aided Breast Cancer Detection System Research

Breast cancer is the most common malignancy in females. Early diagnosis and treatmentcan increase the survival rate. Mammography is currently the most effective method to detectearly breast disease, by using the low dose X-ray to check the breasts. The radiologists diagnosebreast cancer according to the abornalmality in mammography such as micro-calcifications andmasses. For the radiologistes, however, great clinical experiences are requested to read themammography images, as well as their diagnosis could be affected by many subjective factors.Therefore, it is necessary to develop reliable computer aided diagnosis (CAD) to overcome suchlimitations.In this thesis, we investigated the key CAD techniques related to calcification and massdetection and diagnosis in mamography and solved some problems from current algorithm inbreast cancer detection. The related works are carried out as follows:1. Image enhancement is widely used in calcification detection. But current algorithm couldaffect the correct diagnosis by enhancing imate features and noises together. In order to processthe mammography image compressed by JPEG, we investigated the image enhancementtechnique based on DCT domain and proposed a new algorithm. In this algorithm, each DCTdomain is enhanced firstly according to the contrast value and visual quality given by the user.Then the whole image is decompressed and enhanced by the optimized parameters from geneticalgorithm. This new algorithm can decrease the artifact due to enhacement effection, which hasbeen tested by objective and subjective detection, which can significantly improve theidentification of calcification in breast by radiologists.2. Manual and semi-automatic segmentation are mainly used in mass segmentation.However, manual segmentation lack efficiency, semi-automatic method still needs the manualintervention. We tried to combine the two common methods together and proposed a fullyautomatic algorithm for breast mass segmentation. The mass is roughly segmented by themarked watershed algorithm, and then precisely segmented by our improved level set activecontour. The new algorithm combines the fast running speed of watershed and precisesegmentation of level set, which ensure the fast and efficient segmentation. Moreover, thesealgorithms possess good topological flexibility, enabling segmentation of mass with complicatedcontour.3. It is known that a typical benign mass has a round, smooth, and with well-circumscribedboundary, while a malignant tumor usually has a spiculated, rough and blurry boundary. Thus, boundary analysis has been widely used for the benign or malignant classification of masses.After the segmentation of mass region, statistics, geometry and texture features have beenextracted. Other than these, we proposed a group of new features from gradient information,which are extracted from he boundary of the mass and margin region between mass andbackground to express the spiculation based on the relative gradient orientation of mass contourpixels. Such features could increase the accuracy rate. We also analyzed the texture features andproposed an improved local binary pattern (ILBP) operater on the basis of classical local binarypattern (LBP). ILBP operater chooses the median of image block as the new threshold andmaintains the information of center pixel value. The classification resutls based on the ILBPfeatures extracted from1×1to9×9image blocks showes that, the accurate ration forclassification based on new features increases5%comparing to that on the original LBPfeatures.4) Mass classification is the major support for the CAD in breast cancer diagnosis. Mostmass classification is made on the single or improved classifier, a few is on the integratedclassifier. To satisfy the requst of universality and robustness for mass features, we trained theclassifier by integrated features. The single classifier by machine learning methods isinverstigated, including linear discriminant analysis (LDA) and support vector machine (SVM).We also tested and estimated the above methods in the large data set. The random forest (RF) isfirst used for mass classification. Our work provides solid support for the precise detection andclassification of abnormality in breast mass.5) On the basis of the above research, we design and realize a breast mass images retrevialsystem based on texture features. This system cans calcualte the similarity between imput imageand sample image according to the texure features of input image. Features extraction, inquiry,matching and display can function well in this system. It provids the visual assistance toradiologists by mimicing their diagnosis process and return similar reference case image.