The work focuses on a unique medical repository of digital cervicographic images ("Cervigrams") collected by the National Cancer Institute (NCI) in longitudinal multiyear studies. NCI, together with the National Library of Medicine (NLM), is developing a unique web-accessible database of the digitized cervix images to study the evolution of lesions related to cervical cancer. Tools are needed for automated analysis of the cervigram content to support cancer research. We present a multistage scheme for segmenting and labeling regions of anatomical interest within the cervigrams. In particular, we focus on the extraction of the cervix region and fine detection of the cervix boundary; specular reflection is eliminated as an important preprocessing step; in addition, the entrance to the endocervical canal (the "os"), is detected. Segmentation results are evaluated on three image sets of cervigrams that were manually labeled by NCI experts.

In this paper, we combine discrete and continuous image models with information-theoretic-based criteria for unsupervised hierarchical image-set clustering. The continuous image modeling is based on mixture of Gaussian densities. The unsupervised image-set clustering is based on a generalized version of a recently introduced information-theoretic principle, the information bottleneck principle. Images are clustered such that the mutual information between the clusters and the image content is maximally preserved. Experimental results demonstrate the performance of the proposed framework for image clustering on a large image set. Information theoretic tools are used to evaluate cluster quality. Particular emphasis is placed on the application of the clustering for efficient image search and retrieval.

The National Cancer Institute has collected a large database of digitized 35 mm slides of the uterine cervix, the idea being to build a system enabling to study the evolution of lesions related to cervical cancer. In taking the first few steps towards this goal, the objective of this work is to develop and evaluate methodologies required for visual-based (i.e. content-based) indexing and retrieval that substantially improve information management of such a database. In this paper we model the properties of three tissue types using color and texture features, and use these models for image segmentation. Statistical modeling and segmentation tools are used for the task.

We present two new methods for approximating the Kullback-Liebler (KL) divergence between two mixtures of Gaussians. The first method is based on matching between the Gaussian elements of the two Gaussian mixture densities. The second method is based on the unscented transform. The proposed methods are utilized for image retrieval tasks. Continuous probabilistic image modeling based on mixtures of Gaussians together with KL measure for image similarity, can be used for image retrieval tasks with remarkable performance. The efficiency and the performance of the KL approximation methods proposed are demonstrated on both simulated data and real image data sets. The experimental results indicate that our proposed approximations outperform previously suggested methods.

We present a method for unsupervised clustering of image databases. The method is based on a recently introduced information-theoretic principle, the information bottleneck (IB) principle. Image archives are clustered such that the mutual information between the clusters and the image content is maximally preserved. The IB principle is applied to both discrete and continuous image representations, using discrete image histograms and probabilistic continuous image modeling based on mixture of Gaussian densities, respectively. Experimental results demonstrate the performance of the proposed method for image clustering on a large image database. Several clustering algorithms derived from the IB principle are explored and compared.