In this work we present a method for the interactive feature space exploration and content-based database visualization (CBDV) of medical image databases. Using Self Organizing Maps it is possible to visualize the content of a medical image database. This visualization provides the basis for an interactive visual exploration of the meaning of image features and a characterization of the database content.
The assessment of similarities of breast tumors in DCE-MRI is an important step to improving diagnostic accuracy. A comparison of a breast lesion with different histologic types of tumors can in addition provide further clinical information on the nature of the lesion itself. We present an approach to the visual comparison of different histologic types of breast tumor utilizing Locally Linear Embedding (LLE), an algorithm for dimensional data reduction.The experimental dataset contains the time-series of seven benign and seven malignant breast tumors of various histologic types that were manually labeled by an expert physician from a sequence of DCE-MRI volumes. The adopted DCE-MRI protocol involves six consecutive images of the female breast, yielding to a six-dimensional time-series of MR intensity values for each voxel. The set of all time-series from the 14 tumors constitutes a six-dimensional signal space where similar time-series exhibit locality. This high-dimensional
dataset is projected into two dimensions by LLE while preserving the local space topology. In this way similar time-series are mapped onto neighboring data points in the LLE projection. Its visualization with customized colors encoding the histologic information provides a convenient interface for interactive comparison of various breast tumors belonging to different histologic families.
In this paper we apply multiscale entropy (MSE) analysis to data
obtained from magnetic resonance imaging of the female breast.
All cases include lesions that were histologically proven as
malignant tumors. Our results indicate that multiscale entropy
analysis can play an important role in the detection of tumor
tissue when applied to single datasets, but does not allow to
calculate universal morphological features. The performance of
MSE was examined with respect to traditional features such
as difference imaging.