Research On New Methods Of Segmenting Breast Tumors From Ultrasound Images

Breast cancer is the most common malignant lesions and is the leading cause ofdeath in women. Prevention and treatment of breast cancer adopts three principlesclinically: early detection, early diagnosis and early treatment. Ultrasound (US) imaginghas been one of the main measures of clinical diagnosis on breast tumor due to itsnon-invasive nature, minimal ionizing radiation and low cost. Tumor segmentation ofbreast US image can provide aided diagnosis and second opinion. This improves theobjectivity and accuracy of diagnosis and reduces the likelihood of misdiagnosis andmissed diagnosis.However, accurately segmenting breast tumors in US images is a very difficult taskdue to the following reasons. First, US image has inherent speckle, fuzzy or evenmissing edges, low signal-to-noise ratio, and low contrast due to the effect of USimaging device. Second, there are characteristic artifacts, such as attenuation and thosecaused by nonuniform beam attenuation within the body in radio-frequency field. Third,tumor variance in shape, size and location differs greatly. The effect of tumorinfiltrating is also a reason, that is, the tumor often infiltrates into its surroundingnormal tissue. This leads to the presence of tumor-like structures in malignant tumorimage such as, subcutaneous fat and glandular tissue. It is difficult to distinguish themalignant tumor from these tumor-like structures visually and hence, tumorsegmentation task is much more difficult. All characteristics of breast US images givenabove make US image segmentation very challenging. Segmentation of ultrasonictumor is worthy of further study.Based on the graph theory, curve evolution theory and level set method, this papermakes in-depth study on Normalized Cut (NCut) graph-based method, active contourlevel set method and their applications in two-dimensional(2-D) orthree-dimensional(3-D) US image segmentation. The main contents and innovationinclude the following three aspects:1. Phase-and GVF-based level set segmentation of ultrasonic breast tumorsBased on the distance regularized level set evolution model, this paper presents a phase-and GVF-based level set method for segmentation of ultrasonic breast tumors.First, by introducing the concept of monogenic signal, we use Cauchy kernels ratherthan Log Gabor as pair of quadrature filters for the multi-scale feature extraction.Second, phase asymmetry approach is then applied to multi-scale features to enhanceedges and remove noise effect. Third, based on precalculated edge map, an edgestopping term is defined and gradient vector flow (GVF) is then improved. At last, theedge stopping term and the resulting GVF are incorporated into the active contourmodel. The proposed method is insensitive to intensity inhomogeneities and noise dueto the use of local phase information. On the other hand, the proposed method cancapture concave boundaries and weak boundaries due to the use of GVF field.Experiments on clinical breast US images showed that the proposed method can extracttumor boundaries from breast US image, as compared to the state-of-the-art methods.2. Segmentation of ultrasonic breast tumors based on homogeneous patchThis paper presents a novel algorithm based on homogeneous patches (HP) andNCut for segmentation of breast tumor in US images. A novel edge-detection functionis defined by combining intensity and texture information to look for boundaries in USimages. Subsequently, a kind of adaptive neighborhood according to image locationreferred to as homogeneous patch (HP), is proposed by using edge map from anedge-detection function. The HPs are guaranteed to spread within the same tissue region.Hence, the statistic features within HPs can better distinguish different tissues and,furthermore, improve accuracy in image segmentation. Each HP is considered as afuzzy set. The fuzzy distribution of textons in HPs is used as final image features andtumor segmentation is obtained by using the NCut method. The proposed method canavoid attenuation artifacts and decrease the likelihood that the surrounding structuresare misclassified as tumor. Experimental results from100breast sonogramsdemonstrated the improvement in accuracy and robustness in segmenting the breastultrasound images by the presented algorithm, as compared to the state-of-the-artmethods.3.3-D segmentation of ultrasonic breast tumors based on homogeneous volumeand local energyBy taking the advantages of the classical Chan-Vese (CV) level set model andLankton method, this paper presents a novel level set active contour model based on homogeneous volume and local energy for3-D segmentation of ultrasonic breast tumors.First,3-D phase asymmetry approach is used to extract edge surface. And then at eachvoxel in3-D image, a quadric surface is fitted to estimate the edge energies based on theprecalculated edge surface. The fitted surface is continuous and smooth and isinsensitivity to noise. Second, based on the precalculated fitted surface, the concept of2-D homogeneous patch (HP) is extended to3-D homogeneous volume (HV), i.e., akind of adaptive neighborhood which can guarantee locally homogeneous neighborhoodin3-D. Third, local energies are defined at each voxel along the curve by usingdistribution of textons and mean intensity within HVs. Similar to HPs, using HVs helpsto discriminate those pixels with similar appearance but belonging to different tissues.At the same time, global energies are defined at each voxel along the curve by usingglobal statistics. When the contour is far from object boundaries, the force from theglobal energies is used to guide the contour toward and finally stops the contour atobject boundaries. At last, the local and global energies are incorporated into thegeometric active contour model and tumor segmentation is obtained by using the fastcurve evolution method. The proposed method can overcome the problem of intensityinhomogeneities and avoid falling into local extrema. Experiments on25clinical3-Dbreast US images showed that the proposed method can segment3-D ultrasonic breasttumors accurately, as compared to the state-of-the-art methods.