Regina Pohle

Segmentation of medical images using adaptive region growing

Interaction increases flexibility of segmentation but it leads to undesirable behavior of an algorithm if knowledge being requested is inappropriate. In region growing, this is the case for defining the homogeneity criterion as its specification depends also on image formation properties that are not known to the user. We developed a region growing algorithm that learns its homogeneity criterion automatically from characteristics of the region to be segmented. The method is based on a model that describes homogeneity and simple shape properties of the region. Parameters of the homogeneity criterion are estimated from sample locations in the region. These locations are selected sequentially in a random walk starting at the seed point, and the homogeneity criterion is updated continuously. The method was tested for segmentation on test images and of structures in CT images. We found the method to work reliable if the model assumption on homogeneity and region characteristics are true. Furthermore, the model is simple but robust, thus allowing for a certain degree of deviation from model constraints and still delivering the expected segmentation result. This approach was extended to a fully automatic and complete segmentation method by using the pixels with the smallest gradient length in the not yet segmented image region as a seed point.

A New Approach for Model-Based Adaptive Region Growing in Medical Image Analysis

Interaction increases flexibility of segmentation but it leads to undesired behaviour of an algorithm if knowledge being requested is inappropriate. In region growing, this is the case for defining the homogeneity criterion as its specification depends also on image formation properties that are not known to the user. We developed a region growing algorithm that learns its homogeneity criterion automatically from characteristics of the region to be segmented. It produces results that are only little sensitive to the seed point location and it allows a segmentation of individual structures. The method was successfully tested on artificial images and on CT images.

Segmentation of 3D medical image data sets with a combination of region-based initial segmentation and active surfaces

Segmentation is an essential step in the analysis of medical images. For segmentation of 3-D data sets in clinical practice segmentation methods are necessary which have a small user interaction time and which are highly flexible. For this purpose we propose a two-step segmentation approach. The first step results in a coarse segmentation using the Image Foresting Transformation. In the second step an active surface creates the final segmentation. Our segmentation method was tested for segmentation on real CT images. The performance was compared with the manual segmentation. We found our work method reliable.

Region-based interactive 3D image analysis of structures in medical data by hybrid rendering

In this paper we present a method for interactive analysis of non-segmented medical volume data. We discuss both different rendering methods for visualization and different possibilities for interaction in relation to segmentation results. Furthermore, the adaptive region growing approach is applied to both segmentation of a structure of interest, as well as generation of transfer function for volume rendering of the same structure. The adaptive region growing method is based on the statistical evaluation of 3D-neighbourhood. The method is used for determination of a homogeneity criterion for the structure of interest. Subsequently this criterion is used for segmenting of data and for generating of an initial transfer function for volume rendering. We utilize this for displaying a hybrid 3D-visualization of the segmented structure and the specific gray-value interval of original data. Based on this rendering we discuss possibilities for user-guided validation of segmentation results, based on the variation of several rendering parameters.

Segmentation of the left ventricle in 4d-dSPECT data using free form deformation of super quadrics

In dynamic SPECT (dSPECT) images, function of a particular organ may be analyzed by measuring the temporal change of the spatial distribution of radioactive tracer. The organ-specific and location-specific time-activity curves (TAC) of the different heart regions (regions with normal blood circulation and with disturbed blood circulation) are helpful for the diagnosis of heart diseases.A problem of the derivation of the TACs is that the dSPECT images have a poor spatial and temporal resolution and the data is distorted because noise effects, partial volume effects and scatter artifacts. Therefore in a preprocessing step the quality of the data is improved with a nonlinear isotropic diffusion in combination with the principal component analysis. Segmentation according to some homogeneity principle will deliver regions of similar functional behavior but the segmented regions do not directly point to anatomy. For our goal of anatomy specific segmentation information about anatomy is provided a-priori and it must be fitted to the data. For initialization the user have to mark six positions of the left ventricle in the data set which are used to place a super ellipsoid. The parameters of this super ellipsoid are obtained from the computed mean shape of six manual segmented left ventricles in test data sets. A closer fit to the high gradients of the boundaries of the heart wall is achieved using the free form deformation method. For evaluation segmentation results are compared with a manual segmentation. In all test images we could ascertain a good correspondence between the manual and automatic segmentation.

Three-level evaluation process for segmentation methods in medical imaging

We propose an evaluation process for segmentation which is made up of three different levels. It enables us to carry out the time consuming steps only for those segmentation methods for which a successful segmentation is foreseeable. In the first level the developer of a segmentation method does a coarse analysis of the usefulness of the individual segmentation methods by means of visual assessment of the results for few image examples. Methods which have been judged useful at the first level are investigated in a second evaluation step as to the stability of the segmentation results in case of slight deviations in the images. For the reproduction of the image formation process a multitude of realizations of a given region of interest are produced by means of the bootstrap technique. At the third level of the evaluation process the segmentation methods are tested for segmentation errors. The segmentation methods are judged by means of empirical discrepancy values, and the effectiveness of a method chosen for the respective task is finally estimated.

Skeletal muscle sonography with texture analysis

In this paper a computer system is presented which is aided to support the physician in the evaluation of muscle ultrasound images. For this purpose a multitude of texture features are calculated for each region of interest (ROl), from which one optimal subset is selected for the different diagnosis problems. The results achieved so far are presented and possibilities for improvement are discussed. Keywords: texture analysis, tissue characterization, feature extraction, ultrasound, neuromuscular diseases

3D-Analyse medizinischer Volumendaten unter Nutzung automatisch generierter Transferfunktionen

In diesem Beitrag wird ein Verfahren zur nutzergefuhrten 3D-Visualisierung von Strukturen innerhalb medizinischer Volumendatensatze vorgestellt. Das Verfahren nutzt das a-priori-Wissen des Anwenders uber die Lage der interessierenden Struktur und berechnet davon ausgehend automatisch eine initiale Transferfunktion fur die direkte Volumenvisualisierung mittels Volume Rendering. Dabei wird ein adaptives Regionenwachstum zur Schatzung des Grauwertintervalles fur die gesuchte Struktur eingesetzt. Es wird eine Beispielimplementierung beschrieben, die es erlaubt, durch eine intuitive Steuerung der Darstellungsparameter fur das Volume Rendering, eine interaktive 3D-Analyse der interessierenden Struktur zu ermoglichen.

Shape-based enhancement of vascular structures in digital subtraction angiography images using local covariance information

We present a new method of enhancing cerebral vessels in subtraction angiography that defines shape attributes in terms of pixel features. Vessel knowledge comprises information on the imaging process, e.g., distribution of contrast media, noise characteristics, and morphological information on the vessels. The latter is computed as a fuzzy measure because pixels have not yet been classified into vessel and background pixels. We model our image as result of a process of projecting discrete contrast media voxels on the image plane. The projection is assumed to be distorted by noise. The shape feature is derived from the Karhunen-Loeve transformation (KLT) that is computed at each pixel from the covariance of the contrast distribution in a given neighborhood. Vessel likelihood is computed from local elongatedness. The latter is derived from the variances along the two principal axes and from the first central moment of the contrast distribution. The directional information from the KLT is used for anisotropic diffusion for noise reduction. Results of the enhancement step on angiographic data showed a significant improvement of the contrast while not blurring the image. Closely neighboring vessels could be differentiated if they were one pixel apart and if the SNR were better than 2:1.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Semi-automatic Segmentation of the Left Ventricle in CINE MR Datasets by Linked Radial Active Model (LRAM)

Heart failures (cardiac infarction) are of increasing importance due to increasing life expectation. For clinical diagnosis parameters for the condition of hearts are needed and can be derived automatically by imaging processing. In this work we present an efficient method to segment the left ventricle (LV) in heart MR data from rats using two linked active contour models working in a spherical coordinate system. The initial model used for the active contour scheme is generated from user given points by a radial interpolation algorithm. The model was developed on healthy heart data and was tested on 15 different data sets and the results are presented.