D. Schlaps

Ultrasonic Tissue Characterization Using a Diagnostic Expert System

Conventional gray scale B-mode ultrasound is a commonly applied and useful method for a large number of diseases. The operator dependency, however, is limiting its clinical usefulness as well as the reproducability of an examination.

Ultrasonic Tissue Characterization By Texture Analysis: Elimination Of Tissue-Independent Factors

When image texture analysis methods are used for ultrasonic tissue characterization, the discrimination results obtained by statistical pattern discrimination methods must be interpreted carefully in order to avoid pseudo-discriminations due to differences in exa-mination procedures and system settings. This study examines the dependence of popular texture analysis methods on transducer-specific diffraction characteristics, B-mode image reconstruction and sampling factors, i.e. size and position of the selected Region-of-Interest. It is shown that image analysis should always be based on diffraction-corrected ultra-sound signals. In large-organ applications, e.g. liver, polar reconstruction yielded more stable results than cartesian reconstruction, especially when texture measures from the greylevel runlength matrices or power spectrum are used. Analyzing clinical and synthesized ultrasound images, we found that the first-order greylevel statistics: Mean greylevel, skewness and excess as well as the second-order sta-tistics: Correlation of greylevel cooccurrences proved to be stable with respect to tissue-independent factors as well as sufficiently sensitive to tissue differences.

Assessing Hyperthermic Treatment Success By Two Dimensional Ultrasound Textural Analysis

In hyperthermia treatment, ability to predict complete tissue temperature fields from a limited sampled temperatures or non invasively is greatly desirable to assess treatment success. Non uniform heating of the lesion causes less pronounced cell kill in inadequately heated regions. Tissues were suspended in a temperature controlled water bath, the temperature of the bath is increased at different high rates to ensure no tissue variations. Two thermistors were inserted at the upper and lower edges of the region of interest (ROI) with-in the tissue sample. A real time sector ultrasound image along with the radio frequency signal are accessed in real time at different temperatures of the (ROI). First and second order grey level statistics were calculated for fresh liver kidney and brain (cow) tissues in the temperature range of 32-47°C. The average temperature in the (ROI) is correlated with the calculated parameters. Entropy is a sensitive parameter to assess the uniformity of heating. Possibility of in vivo study, and to predict long term hyperthermic effects via the calculation of backscattering coefficient is further investigated. A multi-layered model is also presented to aid in spatially resolving temperature dependent acoustic parameters.

Ultrasound Textural Synthesis Using 2-D Autoregressive Models For Pathology Characterization

An autoregressive model is generated for a two dimensional pulsed ultrasound data. The autoregressive parameters are used to differentiate between various liver textures,potential for pathology characterization is discussed. The same model could be used to seperate regions of normal and diseased tissues,subtracting original image out of the developed texture. The theory of 2-D autoregression is developed as well as discussing the overall applicability and drawbacks of the technique.

System zum Vergleich von sonographischer Textur und histologischem Aufbau

Mit Hilfe der Sonographie konnen aufgrund der Wechselwirkungen zwischen Gewebe und Ultraschallwellen Organstrukturen und umschriebene Prozesse makroanatomisch bis in Millimeterbereiche abgegrenzt werden. Die Ultraschallbildtextur, d. h. die Anordnung und Helligkeit der Bildechos, wird dagegen durch den feingeweblichen Aufbau bestimmt. In diesem Artikel wird ein computerunterstutztes System vorgestellt, das eine Verbindung zwischen der quantitativen Texturanalyse des Ultraschallbildes und der quantitativen Analyse eines histologischen Gewebeschnittes ermoglicht (Abb. 1).

Computer-Assisted Echographic Tissue Characterization in Tumor Diagnostics

The echographic characterization of tissue is open to three different biophysical approaches: 1. The analysis of the radio-frequency signal (RF-signal) 2. The analysis of the video-A signal 3. The analysis of the B-scan A hardware-software system for the acquisition and evaluation of ultrasonic data was developed in our institute. This system permitted the quantitative analysis of a series of A-scans by means of distinct statistical parameter sets leading to the differentiation of normal tissue from cirrhotic and metastatic liver [1,3].

An Image Analysis Workstation for Ultrasonic Tissue Characterization

Conventional ultrasound imaging is a frequently applied and useful method for the diagnosis of a wide variety of diseases. However, in the literature there is some discussion about considerable operator variation being observed originating from operator dependency and leading to varying diagnostic accuracies. Therefore, to overcome the reproducability problem, as well as to inhance the diagnostic value of the imaging technique, it is clinically useful to explore the computer assisted approach to tissue characterization.