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author:("Liu, jinfeng")
1.  Breast Ultrasound Image Classification Based on Multiple-Instance Learning 
Journal of Digital Imaging  2012;25(5):620-627.
Breast ultrasound (BUS) image segmentation is a very difficult task due to poor image quality and speckle noise. In this paper, local features extracted from roughly segmented regions of interest (ROIs) are used to describe breast tumors. The roughly segmented ROI is viewed as a bag. And subregions of the ROI are considered as the instances of the bag. Multiple-instance learning (MIL) method is more suitable for classifying breast tumors using BUS images. However, due to the complexity of BUS images, traditional MIL method is not applicable. In this paper, a novel MIL method is proposed for solving such task. First, a self-organizing map is used to map the instance space to the concept space. Then, we use the distribution of the instances of each bag in the concept space to construct the bag feature vector. Finally, a support vector machine is employed for classifying the tumors. The experimental results show that the proposed method can achieve better performance: the accuracy is 0.9107 and the area under receiver operator characteristic curve is 0.96 (p < 0.005).
doi:10.1007/s10278-012-9499-x
PMCID: PMC3447095  PMID: 22733258
Multiple-instance learning (MIL); Breast ultrasound (BUS) image; SVM (support vector machine); Classification
2.  An automated and simple method for brain MR image extraction 
Background
The extraction of brain tissue from magnetic resonance head images, is an important image processing step for the analyses of neuroimage data. The authors have developed an automated and simple brain extraction method using an improved geometric active contour model.
Methods
The method uses an improved geometric active contour model which can not only solve the boundary leakage problem but also is less sensitive to intensity inhomogeneity. The method defines the initial function as a binary level set function to improve computational efficiency. The method is applied to both our data and Internet brain MR data provided by the Internet Brain Segmentation Repository.
Results
The results obtained from our method are compared with manual segmentation results using multiple indices. In addition, the method is compared to two popular methods, Brain extraction tool and Model-based Level Set.
Conclusions
The proposed method can provide automated and accurate brain extraction result with high efficiency.
doi:10.1186/1475-925X-10-81
PMCID: PMC3180437  PMID: 21910906

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