Andrzej Materka

MaZda-A software package for image texture analysis

MaZda, a software package for 2D and 3D image texture analysis is presented. It provides a complete path for quantitative analysis of image textures, including computation of texture features, procedures for feature selection and extraction, algorithms for data classification, various data visualization and image segmentation tools. Initially, MaZda was aimed at analysis of magnetic resonance image textures. However, it revealed its effectiveness in analysis of other types of textured images, including X-ray and camera images. The software was utilized by numerous researchers in diverse applications. It was proven to be an efficient and reliable tool for quantitative image analysis, even in more accurate and objective medical diagnosis. MaZda was also successfully used in food industry to assess food product quality. MaZda can be downloaded for public use from the Institute of Electronics, Technical University of Lodz webpage.

Effects of MRI acquisition parameter variations and protocol heterogeneity on the results of texture analysis and pattern discrimination: an application-oriented study.

MRI texture features are generally considered to be sensitive to variations in signal-to-noise ratio and spatial resolution, which represents an obstacle for the widespread clinical application of texture-based pattern discrimination with MRI. This study investigates the sensitivity of texture features of different categories (co-occurrence matrix, run-length matrix, absolute gradient, autoregressive model, and wavelet transform) to variations in the number of acquisitions (NAs), repetition time (TR), echo time (TE), and sampling bandwidth (SBW) at different spatial resolutions. Special emphasis was placed on the influence of MRI protocol heterogeneity and implications for the results of pattern discrimination. Experiments were performed using two polystyrene spheres and agar gel phantoms with different nodular patterns. T2-weighted multislice multiecho images were obtained using a 3.0 T scanner equipped with a microimaging gradient insert coil. Linear discriminant analysis and k nearest neighbor classification were used for texture-based pattern discrimination. Results show that texture features of all categories are increasingly sensitive to acquisition parameter variations with increasing spatial resolution. Nevertheless, as long as the spatial resolution is sufficiently high, variations in NA, TR, TE, and SBW have little effect on the results of pattern discrimination. Texture features derived from the co-occurrence matrix are superior to features of other categories because they enable discrimination of different patterns close to the resolution limits for the smallest structures of physical texture even for datasets that are heterogeneous with regard to different acquisition parameters, including spatial resolution.

Texture analysis for tissue discrimination on T1-weighted MR images of the knee joint in a multicenter study: Transferability of texture features and comparison of feature selection methods and classifiers.

To investigate the reproducibility and transferability of texture features between MR centers, and to compare two feature selection methods and two classifiers.

Two-phase active contour method for semiautomatic segmentation of the heart and blood vessels from MRI images for 3D visualization

The paper presents an active-contour segmentation method for 2D structures in MR images. The method combines two approaches to active contour segmentation, known as balloons and snakes. This makes the method shape independent and accurate. New anti-tangling features were introduced to improve segmentation of very complex object shapes, e.g. the left ventricle with papillary muscles. The method was applied to segment all large structures in the cardiovascular system and its outcome was used for 3D visualization.

Mazda - a software for texture analysis

This paper presents MaZda software for quantitative image texture analysis. This software, primarily developed for classification of magnetic resonance images, can be applied for wide class of textured images including color ones and 3D data. It enables estimation of almost 300 texture features; includes procedures for their reduction and classification. Feature clustering is also provided. The software has been developed since 1998. Currently it is a reliable and efficient tool used by many research institutes for different image analysis tasks.

Remote guidance for the blind — A proposed teleassistance system and navigation trials

The paper presents initial research on the system for remote guidance of the blind. The concept is based on the idea that a blind pedestrian can be aided by spoken instructions from an operator who receives a video stream from a camera carried by the visually impaired user. An early prototype utilizing two laptop PCs and a wireless Internet connection is used in orientation and mobility trials, which aim to measure the potential usefulness of the system and discover possible problems with user-operator communication or device design. Test results show a quantitative performance increase when traveling with a remote guide: 15-50% speed increase and nearly halved times of navigational tasks; however, the main success is the engendered feeling of safety when assisted and the enthusiasm with which the concept was welcomed by blind trial participants.

A Virtual Keypad Based on Alternate Half-Field Stimulated Visual Evoked Potentials

A virtual 8-key keypad has been designed and constructed to experimentally validate a new kind of noninvasive brain computer interface (BCI). The principle of its operation is based on a technique of alternate visual half-field stimulation, proposed recently by second of the authors. Microcontroller and DSP integrated circuits are used to generate the signals driving light-emitting diodes for visual stimulation and to detect brain response in the EEG signal. The EEG signal is measured from 3 electrodes placed on occipital part of the scalp using a battery- operated amplifier and A/C converter. A Bluetooth chip is used to transmit the stream of measured data to the processor. Tests of the keypad operation, involving 10 volunteers, show it is faster than conventional BCI devices based on steady-state visual evoked potentials, reported so far in the literature.

3D image texture analysis of simulated and real-world vascular trees

A method is proposed for quantitative description of blood-vessel trees, which can be used for tree classification and/or physical parameters indirect monitoring. The method is based on texture analysis of 3D images of the trees. Several types of trees were defined, with distinct tree parameters (number of terminal branches, blood viscosity, input and output flow). A number of trees were computer-simulated for each type. 3D image was computed for each tree and its texture features were calculated. Best discriminating features were found and applied to 1-NN nearest neighbor classifier. It was demonstrated that (i) tree images can be correctly classified for realistic signal-to-noise ratio, (ii) some texture features are monotonously related to tree parameters, (iii) 2D texture analysis is not sufficient to represent the trees in the discussed sense. Moreover, applicability of texture model to quantitative description of vascularity images was also supported by unsupervised exploratory analysis. Eventually, the experimental confirmation was done, with the use of confocal microscopy images of rat brain vasculature. Several classes of brain tissue were clearly distinguished based on 3D texture numerical parameters, including control and different kinds of tumours - treated with NG2 proteoglycan to promote angiogenesis-dependent growth of the abnormal tissue. The method, applied to magnetic resonance imaging e.g. real neovasculature or retinal images can be used to support noninvasive medical diagnosis of vascular system diseases.

Feasibility of texture analysis for the assessment of biochemical changes in meniscal tissue on T1 maps calculated from delayed gadolinium-enhanced magnetic resonance imaging of cartilage data: comparison with conventional relaxation time measurements

Objectives:To (1) establish the feasibility of texture analysis for the in vivo assessment of biochemical changes in meniscal tissue on delayed gadolinium-enhanced magnetic resonance imaging of cartilage (dGEMRIC), and (2) compare textural with conventional T1 relaxation time measurements calculated from dGEMRIC data (“T1(Gd) relaxation times”). Materials and Methods:We enrolled 10 asymptomatic volunteers (7 men and 3 women; mean age, 27.2 ± 4.5 years), without a history of meniscus damage, in our study. MRI of the right knee was performed at 3.0 T. An isotropic, 3-dimensional (3D), double-echo steady-state sequences was used for morphologic evaluation, and a dual flip angle 3D gradient echo sequence was used for T1(Gd) mapping. All MRI scans were performed 90 minutes after injection of 0.2 mmol/kg of Gd-diethylenetriamine pentaacetic acid (DTPA)2−, and subsequently, during application of a compressive force (50% of the body weight) in the axial direction. Regions of interest, covering the central portions of the posterior horn of the medial meniscus, were defined on 3 adjacent sagittal sections. Based on the relaxation time maps, mean T1(Gd), as well as the T1(Gd) texture features derived from the co-occurrence matrix (COC: Angular Second Moment, Entropy, Inverse Difference Moment) and wavelet transform (WAV: WavEnLL, WavEnHL, WavEnHH, WavEnLH), were calculated. Paired t tests were used to assess differences between baseline and compression, and intraclass correlation coefficients (ICC) were calculated to establish the intrarater reliability of the measurements. Results:Mean T1(Gd) (−67.3 ms, P = 0.011), Angular Second Moment (−0.0002, P = 0.009), Entropy (+0.033, P = 0.025), WavEnLL (+1011.16, P = 0.002), WavEnHL (+18.64, P = 0.012), and WavEnLH (+72.74, P = 0.035) differed significantly between baseline and compression. Intrarater reliability was substantial for mean T1(Gd) relaxation times (ICC = 0.99–1.0), and also for T1(Gd) co-occurrence matrix (ICC = 0.63–0.92) and WAV (ICC = 0.86–0.98) features. Conclusions:Texture features extracted from T1 maps calculated from dGEMRIC data are feasible for the in vivo assessment of biochemical changes in the menisci, such as might be induced by mechanical loading. Thus, T1(Gd) texture features complement conventional relaxation time measurements. Further studies are necessary to determine whether the mechanical compression, or a prolonged Gd-DTPA2− uptake, or both, are responsible for the observed decrease in mean T1(Gd) relaxation times in the menisci.

Computer Simulation of Magnetic Resonance Angiography Imaging: Model Description and Validation

With the development of medical imaging modalities and image processing algorithms, there arises a need for methods of their comprehensive quantitative evaluation. In particular, this concerns the algorithms for vessel tracking and segmentation in magnetic resonance angiography images. The problem can be approached by using synthetic images, where true geometry of vessels is known. This paper presents a framework for computer modeling of MRA imaging and the results of its validation. A new model incorporates blood flow simulation within MR signal computation kernel. The proposed solution is unique, especially with respect to the interface between flow and image formation processes. Furthermore it utilizes the concept of particle tracing. The particles reflect the flow of fluid they are immersed in and they are assigned magnetization vectors with temporal evolution controlled by MR physics. Such an approach ensures flexibility as the designed simulator is able to reconstruct flow profiles of any type. The proposed model is validated in a series of experiments with physical and digital flow phantoms. The synthesized 3D images contain various features (including artifacts) characteristic for the time-of-flight protocol and exhibit remarkable correlation with the data acquired in a real MR scanner. The obtained results support the primary goal of the conducted research, i.e. establishing a reference technique for a quantified validation of MR angiography image processing algorithms.