Daniel Bloyet

A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR images

A framework of fuzzy information fusion is proposed in this paper to automatically segment tumor areas of human brain from multispectral magnetic resonance imaging (MRI) such as T1-weighted, T2-weighted and proton density (PD) images. A priori knowledge about tumors described by radiology experts for different types of MRI are very helpful to guide a automatic and a precise segmentation. However, the terminology used by radiology experts are variable in term of image signal. In order to benefit of these descriptions, we propose to modellize them by fuzzy models. One fuzzy model is built for one type of MRI sequence. The segmentation is finally based on a fusion of different fuzzy information obtained from different types of MRI images. Our algorithm consists of four stages: the registration of multispectral MR images, the creation of fuzzy models describing the characteristics of tumor, the fusion based on fuzzy fusion operators and the adjustment by fuzzy region growing based on fuzzy connecting. The comparison between the obtained results and the hand-tracings of a radiology expert shows that the proposed algorithm is efficient. An average probability of correct detection 96% and an average probability of false detection 5% are obtained through studies of four patients.

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). From our simulations, this criterion is shown to outperform other indices used in the literature. The influence of the fuzziness index was studied using simulated activation combined with real life noise data acquired from subjects under a resting state. Receiver Operating Characteristics (ROC) methodology is implemented to assess the performance of the proposed UFCA with respect to the fuzziness index. An interval of choice around 2, a value widely used in FCA, is shown to yield the best performance.

Fuzzy Markovian segmentation in application of magnetic resonance images

In this paper, we present a fuzzy Markovian method for brain tissue segmentation from magnetic resonance images. Generally, there are three main brain tissues in a brain dataset: gray matter, white matter, and cerebrospinal fluid. However, due to the limited resolution of the acquisition system, many voxels may be composed of multiple tissue types (partial volume effects). The proposed method aims at calculating a fuzzy membership in each voxel to indicate the partial volume degree, which is statistically modeled. Since our method is unsupervised, it first estimates the parameters of the fuzzy Markovian random field model using a stochastic gradient algorithm. The fuzzy Markovian segmentation is then performed automatically. The accuracy of the proposed method is quantitatively assessed on a digital phantom using an absolute average error and qualitatively tested on real MRI brain data. A comparison with the widely used fuzzy C-means algorithm is carried out to show numerous advantages of our method.

Fully automatic identification of AC and PC landmarks on brain MRI using scene analysis

Describes a method for identification of brain structures from MRI data sets. The bulk of the paper concerns an automatic system for finding the anterior and posterior commissures [(AC) and (PC)] in the midsagittal plane. These landmarks are key for the definition of the Talairach space, commonly used in stereotactic neurosurgery, in the definition of common coordinate systems for the pooling of functional positron emission tomography (PET) images and for neuroanatomy studies. The process works according to a step-by-step procedure: it first analyzes the skull limits. A grey-level histogram is then calculated and allows an automated selection of thresholds. Then, the interhemispheric plane is detected. Following an advanced scene analysis in the midsagittal plane for anatomical structures, the AC and the PC are identified. Experimentally, with a set of 200 patients, the process never failed. Its performances and limits are comparable to that of neuroanatomy experts. Those results are due to a high degree of robustness at each step of the program.

Suspended epitaxial YBaCuO microbolometers fabricated by silicon micromachining: Modeling and measurements

Suspended epitaxial YBaCuO microbolometers were successfully fabricated by two silicon micromachining techniques. The first one used the reactive ion etching (RIE) of Si substrates and the second one the etching of the SiO2 layer in separated by implanted oxygen (SIMOX) substrates. This work aims at the modeling and the measurement of the bolometric performances of IR pixels (100×100 μm2 detection area) constituted by suspended bridges in series. The influence of both the dimensions and the thermophysical properties of the materials constituting the membrane is discussed. Thermal conductances and time constants were measured as functions of the length and the width of different suspended bridges fabricated by RIE. Comparison of a “RIE type” bridge with a bridge of same dimensions fabricated from a SIMOX substrate shows that the sensitivity-bandwidth product of the SIMOX bridge is improved by one order of magnitude. All measurements on suspended bridges are consistent with calculations from thermal model. ...

Generalized region growing operator with optimal scanning: application to segmentation of breast cancer images

Segmentation of medical images is a complex problem owing to the large variety of their characteristics. In the automated analysis of breast cancers, two image classes may be distinguished according to whether one considers the quantification of DNA (grey level images of isolated nuclei) or the detection of immunohistochemical staining (colour images of histological sections). The study of these image classes generally involves the use of largely different image processing techniques. We therefore propose a new algorithm derived from the watershed transformation enabling us to solve these two segmentation problems with the same general approach. We then present visual and quantitative results to validate our method.

Knowledge-based segmentation and labeling of brain structures from MRI images

In this paper, we propose a new knowledge-based method illustrated in the context of segmentation, which labels internal brain structures viewed by magnetic resonance imaging (MRI). In order to improve the accuracy of the labeling, we introduce a fuzzy model of regions of interest (ROI) by analogy with the electrostatic potential distribution, to represent more appropriately the knowledge of distance, shape and relationship of structures. The knowledge is mainly derived from the Talairach stereotaxic atlas. The labeling is achieved by the regionwise labeling using genetic algorithms (GAs), followed by a voxelwise amendment using parallel region growing. The fuzzy model is used both to design the fitness function of GAs, and to guide the region growing. The performance of our proposed method has been quantitatively validated by six indices with respect to manually labeled images.

Image segmentation via multiple active contour models and fuzzy clustering with biomedical applications

We address the problem of automatically segmenting cell nuclei or cluster of cell nuclei in image medical microscopy. We present a system of automatic segmentation combining fuzzy clustering and multiple active contour models. An automatic initialization algorithm based on fuzzy clustering is used to robustly identify and classify all possible seed regions in the image. These seeds are propagated outward simultaneously to localize the final contours of all objects. We present examples of quantitative segmentation on biomedical images: segmentation of lobules in color images of histology and segmentation of nuclei in cytological images.

Using active contours and mathematical morphology tools for quantification of immunohistochemical images

An image segmentation method is proposed, which combines mathematical morphology tools and active contours in two stages. First, contours are coarsely approximated by means of morphological operators. Second, these initial contours evolve under the influence of geometric and grey-level information, owing to the model of active contours. The performance of the method is evaluated according to the noise and is compared to the watershed algorithm. Then an application is finally presented for biomedical images of tumour tissue.

Segmentation of magnetic resonance images using fuzzy Markov random fields

We present a fuzzy Markovian method for brain tissue segmentation from magnetic resonance images. Generally, there are three principal brain tissues in a brain dataset: gray matter, white matter and cerebrospinal fluid. However, due to the limited resolution of the acquisition system, many voxels may be composed of multiple tissue types (partial volume effects). The proposed method aims to calculate the fuzzy membership of each voxel to indicate the partial volume degree using a fuzz, Markovian segmentation. Since our method is unsupervised, it first estimates the fuzzy Markovian random field model parameters using a stochastic gradient algorithm. The efficiency of the proposed method is quantified on a digital phantom using an absolute average error, and qualitatively tested on real MRI brain data.