Ultrasound Image Classification Of Superficial Organs Based On Multiple-instance Learning

Cancer is a serious threat to human health. Its successful cure is dependent onearly detection and treatment. Ultrasound imaging is noninvasive, economic,convenient, no radiation and so on. It has become the preferred method for earlydetection of superficial organ diseases. However, correct interpretation for theultrasound images is dependent on the physician’s knowledge level and clinicalexperience, has strong subjectivity. Computer aided diagnosis technology canprovides objective, quantification and decision making information about lesion.It is helpful to eliminate misdiagnosis in clinical practice due to subjective factors.It has become a hot issue in the medical domain. Moreover, it has importantsignificance for improving diagnosis accuracy. At present, there are twoproblems in the computer aided diagnosis system for ultrasound images. One isvery difficult to position lesions precisely due to the image quality. The error canaffect feature extraction and classification performance. The other is the lack ofstudy for classification method under the inaccurate positioning condition.To solve above problems, this dissertation study the classification method ofcomputer-aided diagnosis system for ultrasound images, which do not rely onlesions precisely positioning. It proposes to convert the classification problem ofultrasound image to multiple-instance learning problem. It proposes locallyweighted Citation-kNN algorithm based on sample distribution. There existscrossover of benign and malignant lesions on ultrasound images. Thephenomenon is taking into account and the multiple-instance learning method,which based on mapping sample space to concept space, is proposed. Thedissertation proposes a quantitative criterion for evaluating elastogram. Based onthe characteristics of the B-mode images and elastogram, it proposes amultiple-instance learning method which combining global and local features.The main work includes the following three aspects:1. Locally weighted Citation-kNN algorithm is proposed. By dividing theultrasound image, the whole image is a bag and sub-regions are instances. Thenthe classification problem of ultrasound image can convert to a multiple-instancelearning problem. By further considering spatial distribution of samples, the voter weighted according to distance and dispersion based on traditionalCitation-kNN’s voting set. The different weighting methods are combined andtested. The good results are achieved when experimented on breast ultrasoundimage database and benchmark for multiple-instance learning.2. The classification method for ultrasound images which combining localfeatures and multiple-instance learning is proposed. The non-diseased tissue caninterfere with accurate classification of lesions. To position lesion will help toimprove the classification accuracy. The lesion is roughly located as region ofinterest (ROI) and the ROI is divided. Local texture features are used to describethe ROI. The ROI is a bag and sub-regions are instances. The traditionaldefinition of multiple-instance learning is no longer applicable due to thefeatures’ overlapping between benign and malignant lesion. Clustering method isused to construct the concept space. The bag is projected to the concept space.The classifier is trained on the concept space. The experiments show that theproposed method has higher classification accuracy on the ultrasound imagedatabase.3. The quantitative criterion to evaluate thyroid elastogram is proposed. Amultiple-instance learning method is proposed which combining global and localfeatures. A quantitative indicator for evaluating elastogram is presented byanalyzing elastogram features. It has higher accuracy and reliability comparedwith current clinical evaluation methods. For the elastogram, the global featuresare more discriminable for benign and malignant lesions than local features. Afterlocated the lesion roughly, the global features of elastogram and the local featuresof B-mode images are combined. The proposed multiple-instance learningmethod has achieved better classification accuracy than others.The research work is mainly on classification of ultrasound image. It solves thefeatures extraction and classification problem when imprecisely positioning thelesion well. In addition, it has positive significance to the computer-aideddiagnosis system.