Mathematical morphologic segmentation dedicated to quantitative immunohistochemistry.

Objective: To develop automatic segmentation sequences for fully automated quantitative immunohistochemistry of cancer cell nuclei by image analysis.

Study Design: The study focused on the automated delineation of cancer cell lobules and nuclei, taking breast carcinoma as an example. A hierarchic segmentation was developed, employing mainly the chaining of mathematical morphology operators.

On the number of clusters and the fuzziness index for unsupervised FCA application to BOLD fMRI time series.

The aim of this paper is to present an exploratory data-driven strategy based on Unsupervised Fuzzy Clustering Analysis (UFCA) and its potential for fMRI data analysis in the temporal domain. The a priori definition of the number of clusters is addressed and solved using heuristics. An original validity criterion is proposed taking into account data geometry and the partition Membership Functions (MFs).

Brain tissue classification of magnetic resonance images using partial volume modeling.

This paper presents a fully automatic three-dimensional classification of brain tissues for Magnetic Resonance (MR) images. An MR image volume may be composed of a mixture of several tissue types due to partial volume effects. Therefore, we consider that in a brain dataset there are not only the three main types of brain tissue: gray matter, white matter, and cerebro spinal fluid, called pure classes, but also mixtures, called mixclasses.

A multistep unsupervised fuzzy clustering analysis of fMRI time series.

A paradigm independent multistage strategy based on the Unsupervised Fuzzy Clustering Analysis (UFCA) and its potential for fMRI data analysis are presented. The influence of the fuzziness index is studied using Receiver Operating Characteristics (ROC) methodology and an interval of choice, around the widely used value 2, is shown to yield the best performance. The ill-balanced data problem is also overcome using a pre-processing step to reduce the number of voxels presented to the method.

Automatic morphological sieving: comparison between different methods, application to DNA ploidy measurements.

The aim of the present study is to propose alternative automatic methods to time consuming interactive sorting of elements for DNA ploidy measurements. One archival brain tumour and two archival breast carcinoma were studied, corresponding to 7120 elements (3764 nuclei, 3356 debris and aggregates). Three automatic classification methods were tested to eliminate debris and aggregates from DNA ploidy measurements (mathematical morphology (MM), multiparametric analysis (MA) and neural network (NN)).