S.N. Efstratiadis

Hybrid image segmentation using watersheds and fast region merging

A hybrid multidimensional image segmentation algorithm is proposed, which combines edge and region-based techniques through the morphological algorithm of watersheds. An edge-preserving statistical noise reduction approach is used as a preprocessing stage in order to compute an accurate estimate of the image gradient. Then, an initial partitioning of the image into primitive regions is produced by applying the watershed transform on the image gradient magnitude. This initial segmentation is the input to a computationally efficient hierarchical (bottom-up) region merging process that produces the final segmentation. The latter process uses the region adjacency graph (RAG) representation of the image regions. At each step, the most similar pair of regions is determined (minimum cost RAG edge), the regions are merged and the RAG is updated. Traditionally, the above is implemented by storing all RAG edges in a priority queue. We propose a significantly faster algorithm, which additionally maintains the so-called nearest neighbor graph, due to which the priority queue size and processing time are drastically reduced. The final segmentation provides, due to the RAG, one-pixel wide, closed, and accurately localized contours/surfaces. Experimental results obtained with two-dimensional/three-dimensional (2-D/3-D) magnetic resonance images are presented.

Model-based processing scheme for quantitative 4-D cardiac MRI analysis

Presents an integrated model-based processing scheme for cardiac magnetic resonance imaging (MRI), embedded in an interactive computing environment suitable for quantitative cardiac analysis, which provides a set of functions for the extraction, modeling, and visualization of cardiac shape and deformation. The methods apply 4-D processing (three spatial and one temporal) to multiphase multislice MRI acquisitions and produce a continuous 4-D model of the myocardial surface deformation. The model is used to measure diagnostically useful parameters, such as wall motion, myocardial thickening, and myocardial mass measurements. The proposed model-based shape extraction method has the advantage of integrating local information into an overall representation and produces a robust description of cardiac cavities. A learning segmentation process that incorporates a generating-shrinking neural network is combined with a spatiotemporal parametric modeling method through functional basis decomposition. A multiscale approach is adopted, which uses at each step a coarse-scale model defined at the previous step in order to constrain the boundary detection. The main advantages of the proposed methods are efficiency, lack of uncertainty about convergence, and robustness to image artifacts.

Coronary arterial tree extraction based on artery tracking and mathematical morphology

An algorithm for the unsupervised extraction of the coronary arterial tree in single-view angiograms is proposed. Its output is a structural description of the coronary arterial tree (skeleton and borders) along with accurate information for the coronary artery dimensions. The method consists of two stages. (i) Arterial tree detection, where the approximate centerline and borders of the coronary arterial tree are extracted through a recursive artery tracking method based on circular template analysis for the local artery border detection. (ii) Artery skeleton and border estimation, where the accurate skeleton and borders of each artery segment of the arterial tree are computed based on the morphological tools of homotopy modification and watershed transform. Specifically, the approximate centerline and borders of each artery segment computed at the first stage are used for constructing its enclosing area where the defined skeleton and border curves are considered as markers. Experimental results using digitized coronary angiograms are presented.

Wavelet image compression using IIR minimum variance filters, partition priority, and multiple distribution entropy coding

Image compression methods for progressive transmission using optimal subband/wavelet decomposition, partition priority coding (PPC) and multiple distribution entropy coding (MDEC) are presented. In the proposed coder, hierarchical wavelet decomposition of the original image is achieved using wavelets generated by IIR minimum variance filters. The smoothed subband coefficients are coded by an efficient triple state DPCM coder and the corresponding prediction error is Lloyd-Max quantized. The detail coefficients are coded using a novel hierarchical PPC (HPPC) approach. That is, given a suitable partitioning of their absolute range, the detail coefficients are ordered based on their decomposition level and magnitude, and the address map is appropriately coded. Finally, adaptive MDEC is applied to both the DPCM and HPPC outputs by considering a division of the source of the quantized coefficients into multiple subsources and adaptive arithmetic coding based on their corresponding histograms.

Automated coronary artery extraction using watersheds

An algorithm for the automated extraction of the skeletons and borders of coronary arteries in digitized angiograms is proposed. Initially, the approximate skeleton and borders of the coronary artery tree are extracted through an artery tracking method based on circular template analysis. The skeleton and borders of each artery segment are used for constructing its enclosing area where the defined skeleton and border curves are considered as markers. Using the marked artery segment enclosing area (ASEA), an artery gradient image is constructed where all pixels inside the ASEA, except skeleton ones, are assigned the gradient magnitude of the original image. The markers of the artery gradient image are imposed as its unique regional minima by the homotopy modification method. Then, the watershed transform is applied for extracting the artery segment borders. Experimental results using digitized coronary angiograms are presented.

Artery skeleton extraction using topographic and connected component labeling

In this paper, we propose a method for the detection and extraction of coronary artery skeletons (centerlines) based on the morphological processing of the topographic features of coronary angiogram images. Initially, the angiogram is pre-processed for noise reduction and artery enhancement through directional morphological filtering by reconstruction. The topographic features of the resulting image are detected based on first and second-order image derivatives which characterize the local differential image structure. Using an artery model of a smooth elongated object with an approximately Gaussian smoothed semi-elliptical profile, the candidate skeleton areas are detected as sets of points consisting of ridges, saddle points and peaks. False skeleton areas, produced due to the noise sensitivity of the differentiation filters, have small size and are eliminated by connected component labeling (CCL). CCL may cause the elimination of a few true skeletons which are recovered by the morphological operation of binary reconstruction. Experimental results on clinical coronary angiograms are presented and discussed indicating the robust performance of the proposed method.

Application of a 3-D ischemic heart model derived from MRI data to the simulation of the electrical activity of the heart

The propagation of the electrical activity of the heart is simulated over regions containing infarcted tissue. The method is applied to patients suffering from ischemia, in order to study the impact of the injury on the electrical function of the heart. The spatial distribution of the infarcted tissue and the shape of the endocardial and the epicardial surfaces are derived from MRI data using a semiautomatic method based on 3D Fourier parametric modeling. A 2D grid is then constructed which represents a selected part of the epicardial surface and contains information about the condition of the tissue. This grid is used as input to the simulation algorithm which estimates the ionic currents over time and the propagation of the electric impulse.

Artery skeleton extraction based on consistent curvature labeling

A method for detecting and extracting the skeletons of coronary arteries in coronary angiograms is proposed. The grayscale coronary angiograms are regarded as noisy sampling of the underlying continuous surface. After the application of Gaussian filtering to reduce noise, the topographic features of the smoothed image are detected based on first and second order image derivatives which characterize the local differential image structure. Then, the candidate arteries skeleton areas are detected based on the observation that arteries are smooth elongated objects having approximately a Gaussian smoothed semi-elliptical profile. False skeleton areas are eliminated through connected component analysis and the morphological operation of reconstruction. Experimental results on real digitized coronary angiograms are presented.

Semi-automatic extraction of vascular networks in angiograms

A semi-automatic algorithm for the segmentation of angiographic images is proposed. First, the digital angiogram is smoothed by an image structure preserving technique which is based on homogeneity testing and anisotropic diffusion. Then, the watershed transform is applied to the smoothed image gradient magnitude and the resulting initial image partition is input to a fast hierarchical region merging process. Finally, the vessel regions are extracted from the segmented image by a simple point-and-click interactive procedure. Experimental results on Digital Subtracted Angiograms (DSA) are presented.

Automatic artery skeleton extraction

A new approach to the problem of extracting the skeletons of arteries in angiograms is proposed. The peaks, ridges and saddles of the digitized angiogram are considered as candidate artery skeleton points and are determined by analyzing the first and second order partial derivatives of the angiogram. Next, the binary image of candidate skeleton points is constructed and thinned. The large number of short linear branches, due to noise and irrelevant anatomical structures, are eliminated through connected component labeling of skeleton curves and pruning. Experimental results on digitized angiograms are presented.