Kostas Haris

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 morphological segmentation and labeling of coronary angiograms

A method for extraction and labeling of the coronary arterial tree (CAT) using minimal user supervision in single-view angiograms is proposed. The CAT structural description (skeleton and borders) is produced, along with quantitative information for the artery dimensions and assignment of coded labels, based on a given coronary artery model represented by a graph. The stages of the method are: (1) CAT tracking and detection; (2) artery skeleton and border estimation; (3) feature graph creation; and (iv) artery labeling by graph matching. The approximate CAT centerline and borders are extracted by recursive tracking based on circular template analysis. The accurate skeleton and borders of each CAT segment are computed, based on morphological homotopy modification and watershed transform. The approximate centerline and borders are used for constructing the artery segment enclosing area (ASEA), where the defined skeleton and border curves are considered as markers. Using the marked ASEA, an artery gradient image is constructed where all the ASEA pixels (except the skeleton ones) are assigned the gradient magnitude of the original image. The artery gradient image markers are imposed as its unique regional minima by the homotopy modification method, the watershed transform is used for extracting the artery segment borders, and the feature graph is updated. Finally, given the created feature graph and the known model graph, a graph matching algorithm assigns the appropriate labels to the extracted CAT using weighted maximal cliques on the association graph corresponding to the two given graphs. Experimental results using clinical digitized coronary angiograms are presented.

Watershed-based image segmentation with fast region merging

A hybrid image segmentation algorithm is proposed which combines edge- and region-based techniques through the morphological algorithm of watersheds and consists of the following steps: (a) edge-preserving noise reduction, (b) gradient approximation, (c) detection of watersheds on gradient magnitude image, and (d) hierarchical region merging (HRM) in order to get semantically meaningful segmentations. HRM 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 the RAG edges in a priority queue (heap). We propose a significantly faster algorithm which maintains an additional graph, the most similar neighbor graph, through which the priority queue size and processing time are drastically reduced. In addition, this region based representation provides one-pixel wide, closed, and accurately localized contours/surfaces. Experimental results using 2D real images are presented.

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.

Hybrid image segmentation using watersheds

A hybrid image segmentation algorithm is proposed which combines edge- and region-based techniques through the morphological algorithm of watersheds. The algorithm consists of the following steps: (1) edge-preserving statistical noise reduction, (2) gradient approximation, (3) detection of watersheds on gradient magnitude image, and (4) hierarchical region merging (HRM) in order to get semantically meaningful segmentations. The HRM 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 the RAG edges in a priority queue (heap). We propose a significantly faster algorithm which maintains an additional graph, the most similar neighbor graph, through which the priority queue size and processing time are drastically reduced. The final segmentation is an image partition which, through the RAG, provides information that can be used by knowledge-based high level processes, i.e. recognition. In addition, this region based representation provides one-pixel wide, closed, and accurately localized contours/surfaces. Due to the small number of free parameters, the algorithm can be quite effectively used in interactive image processing. Experimental results obtained with 2D MR images are presented.

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.

Hierarchical image segmentation based on contour dynamics

We propose an image segmentation method based on morphological decomposition and graph-based region merging using contour dynamics. The input image is initially decomposed into a set of primitive homogeneous regions through the morphological watershed transform applied to the image intensity gradient magnitude. This decomposition is represented by a region adjacency graph (RAG) that is input to a hierarchical merging process in which neighboring regions of high similarity are merged. The region similarity criterion is based on the concept of watershed contour dynamics. The robustness of the segmentation to the presence of noise and/or low contrast is improved by a regularization of the contour dynamics. Experimental results on various kinds of synthetic and real images, as well as comparison of the proposed method with other wellknown region merging algorithms 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.

Combining pervasive technologies and Cloud Computing for COPD and comorbidities management

Integrated care of patients with COPD and comorbidities requires the ability to regard patient status as a complex system. It can benefit from technologies that extract multiparametric information and detect changes in status along different axes. This raises the need for generation of systems that can unobtrusively monitor, compute, and combine multiorgan information. In this paper, the concept and ongoing work for such an approach is presented as regards the multiple types of data recorded, features extracted, and examples of how they are combined in the EU-funded project WELCOME (Wearable Sensing and Smart Cloud Computing for Integrated Care to COPD Patients with Comorbidities) [1], for the integrated management of COPD and comorbidities.

Self-gated fetal cardiac MRI with tiny golden angle iGRASP : A feasibility study

To develop and assess a technique for self‐gated fetal cardiac cine magnetic resonance imaging (MRI) using tiny golden angle radial sampling combined with iGRASP (iterative Golden‐angle RAdial Sparse Parallel) for accelerated acquisition based on parallel imaging and compressed sensing.