Mammographic mass segmentation: Embedding multiple features in vector-valued level set in ambiguous regions

Mammographic mass segmentation plays an important role in computer-aided diagnosis systems. It is very challenging because masses are always of low contrast with ambiguous margins, connected with the normal tissues, and of various scales and complex shapes. To effectively detect true boundaries of mass regions, we propose a feature embedded vector-valued contour-based level set method with relaxed shape constraint.In particular, we initially use the contour-based level set method to obtain the initial boundaries on the smoothed mammogram as the shape constraint. To prevent the contour leaking and meanwhile preserve the radiative characteristics of specific malignant masses, afterward, we relax the obtained shape constraint by analyzing possible valid regions around the initial boundaries. The relaxed shape constraint is then used to design a novel stopping function for subsequent vector-valued level set method. Since texture maps, gradient maps, and the original intensity map can reflect different characteristics of the mammogram, we integrate them together to obtain more accurate segmentation by incorporating the new stopping function into the newly proposed feature embedded vector-valued contour-based level set method.The experimental results suggest that the proposed feature embedded vector-valued contour-based level set method with relaxed shape constraint can effectively find ambiguous margins of the mass regions. Comparing against existing active contours methods, the new scheme is more effective and robust in detecting complex masses.     

Investigation of wave atom transform by using the classification of mammograms

This paper presents an approach for breast cancer diagnosis in digital mammograms using wave atom transform. Wave atom is a recent member of the multi-resolution representation methods. Primarily, the mammogram images are decomposed on the basis of wave atoms, and then a special set of the biggest coefficients from wave atom transform is used as a feature vector. Two different classifiers, support vector machine and k-nearest neighbors, are employed to classify mammograms. The method is tested using two different sets of images provided by MIAS and DDSM database.     

Using Renyi's Information and Wavelets for Target Detection: An Application to Mammograms

In this paper we present a multi-scale method for the detection of small targets embedded in noisy background. The multi-scale representation is built using a weighted undecimated discrete wavelet transform. The method, in essence, is based on the maximisation of information available at each resolution level of the representation. We show that such objective can be achieved by maximising Renyi’s information. This approach allows us to determine an adaptive threshold useful for discriminating, at each scale, between wavelet coefficients representing targets and those representing background noise. Eventually, avoiding inverse transformation, scale-dependent estimates are combined according to a majority vote strategy. The proposed technique is experimented on a standard data set of mammographic images.     

乳腺癌诊断的计算机处理研究

介绍了开发医学图象处理系统所涉及到的数据技术,以及用到的图象处理技术。