Michal Strzelecki

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.

Monitoring the survival of islet transplants by MRI using a novel technique for their automated detection and quantification

ObjectThere is a clinical need to be able to assess graft loss of transplanted pancreatic islets (PI) non-invasively with clear-cut quantification of islet survival. We tracked transplanted PI in diabetic mice during the early post-transplant period by magnetic resonance imaging (MRI) and quantified the islet loss using automatic segmentation technique.Materials and methodsMagnetically labeled islet iso-, allo- and xenografts were injected into the right liver lobes. Animals underwent MRI scanning during 14 days after PI transplantation. MR images were processed using custom-made software, which automatically detects hypointense regions representing PI. It is based on morphological top-hat and bottom-hat transforms.ResultsManually and automatically detected areas, corresponding to PI, differed by 4% in phantoms. Signal loss regions due to PI decreased comparably in all groups during the first week post transplant. Throughout the second week post-transplant, the signal loss area continued in a steep decline in case of allografts and xenografts, whereas the decline in case of isografts slowed down.ConclusionAutomatic segmentation allows for the more reproducible, objective assessment of transplanted PI. Quantification confirms the assumption that a significant number of islets are destroyed in the first week following transplantation irrespective of allografts, xenografts or isografts.

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.

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.

Classification of breast thermal images using artificial neural networks

In this paper we present classification of the thermal images in order to discriminate healthy and pathological cases during breast cancer screening. Different image features and approaches for data reduction and classification have been used. The most promised method was based on wavelet transformation and nonlinear neural network classifier.

Segmentation of 3D MR Liver Images Using Synchronised Oscillators Network

Recent development of three-dimensional imaging techniques with application in medical science demands a development of appropriate 3D image analysis techniques. This paper presents a segmentation method based on three-dimensional network of synchronized oscillators applied for 3D MR liver images. Principles of oscillator network operation were described. The network was tested on sample 3D artificial images, one corrupted by noise and distorted by non-uniform illumination and second containing textures. Segmentation results of liver images were compared and discussed with those obtained with the use of multilayer feedforward perceptron (MLP). It was demonstrated that the advantage of the discussed approach is its resistance to changes of visual image information caused for example by noise, very often present in biomedical images.

Parametric testing of mixed-signal circuits by ANN processing of transient responses

It is postulated that feedforward artificial neural networks can be used for fast and robust parametric testing of mixed-signal circuits when applied to the processing of transient waveforms which are circuit responses to test signals. Numerical and experimental results are presented to verify the validity of the technique using examples of OPAMP and OTA-C filters and of a CMOS inverter. A feedforward artificial neural network in the form of a single-hidden-layer sigmoidal perceptron is trained in this preliminary study to estimate the circuit parameters. Since no iterative calculations are performed to identify parameter values with this technique, it is highly suitable for high-speed parametric testing. It is also more robust in the presence of noise when compared to traditional approaches. Topics for future research are addressed.

Implementation of a Synchronized Oscillator Circuit for Fast Sensing and Labeling of Image Objects

We present an application-specific integrated circuit (ASIC) CMOS chip that implements a synchronized oscillator cellular neural network with a matrix size of 32 × 32 for object sensing and labeling in binary images. Networks of synchronized oscillators are a recently developed tool for image segmentation and analysis. Its parallel network operation is based on a “temporary correlation” theory that attempts to describe scene recognition as if performed by the human brain. The synchronized oscillations of neuron groups attract a person’s attention if he or she is focused on a coherent stimulus (image object). For more than one perceived stimulus, these synchronized patterns switch in time between different neuron groups, thus forming temporal maps that code several features of the analyzed scene. In this paper, a new oscillator circuit based on a mathematical model is proposed, and the network architecture and chip functional blocks are presented and discussed. The proposed chip is implemented in AMIS 0.35 μm C035M-D 5M/1P technology. An application of the proposed network chip for the segmentation of insulin-producing pancreatic islets in magnetic resonance liver images is presented.

Numerical Modeling of MR Angiography for Quantitative Validation of Image-Driven Assessment of Carotid Stenosis

In this paper we present a numerical framework for validating methods of quantitative analysis of non-invasive MR angiography imaging protocols such as Time-of-Flight (ToF) and Phase Contrast Angiography (PCA). Our study is motivated by the need to reliably and objectively verify blood flow and geometry measurements derived from image data. Both factors are important predictors in diagnosing of carotid artery stenosis. Credibility of the tested image processing methods is verified by comparing their results against reference models designed using integrated flow and MRA imaging simulator.

Model Based Approach for Melanoma Segmentation

is no suitable golden standard for assessment and comparison of segmentation methods applied to skin lesions images. Thus there is a need for development of image analysis techniques that satisfy at least subjective criteria defined by dermatologists. We present a model based approach for melanocytic image segmentation as a tool to improve computer aided diagnosis. During the research it was necessary to correct non-uniform image illumination caused by dermatoscope lightning. The correction algorithm based on dermatoscope light intensity estimation was used. The proposed segmentation method is based on histogram skin modeling. Preliminary test results are promising, for the analyzed melanoma images mean Jaccard index of 89.48% and mean sensitivity of 92.45% were obtained (when compared to expert assessment).