Automatic feature extraction and analysis on breast ultrasound images

To automatically extract the morphologic features of breast lesion on ultrasound images, K-way normalized cut with the priori rules was applied to perform segmentation, and local area integral invariant was used to analyse the shape of lesion for detecting the structures on the lesion contour and modeling the lesion boundary. Three new morphologic feature measures: mean (AvgLAI), standard deviation (StdLAI), and signal-to-noise ratio (SnrLAI) of the normalized local area integral invariant were proposed to quantify the anfractuosity of lesion shape. Other 132 feature measures were also computed to evaluate the performance of computerized features. These 135 measures characterized the morphologic features, margin features, texture features and acoustic shadowing behavior of the lesions, and were evaluated by ROC analysis on a database of 59 patients. The experimental results showed that the individual morphologic feature measure had the strong ability to distinguish the malignant and benign breast lesions, especially the sensitivity of StdLAI, and SnrLAI could reach 0.92 with the specificity 0.63. It was also found that the discrimination performance of individual feature measure on margin, texture and acoustic shadowing was relatively low on the database.