[Show abstract][Hide abstract] ABSTRACT: Adaptation to exercise training can affect bone marrow adiposity; muscle-fat distribution; and muscle volume, strength and architecture. The objective of this study was to identify exercise-load-associated differences in magnetic resonance image textures of thigh soft tissues between various athlete groups and non-athletes. Ninety female athletes representing five differently loading sport types (high impact, odd impact, high magnitude, repetitive low impact and repetitive non-impact), and 20 non-athletic clinically healthy female controls underwent magnetic resonance imaging. Five thigh muscles, subcutaneous fat and femoral bone marrow were analysed with co-occurrence matrix-based quantitative texture analysis at two anatomical levels of the dominant leg. Compared with the controls thigh muscle textures differed especially in high-impact and odd-impact exercise-loading groups. However, all sports appeared to modulate muscle textures to some extent. Fat tissue was found different among the low-impact group, and bone marrow was different in the high-impact group when compared to the controls. Exercise loading was associated with textural variation in magnetic resonance images of thigh soft tissues. Texture analysis proved a potential method for detecting apparent structural differences in the muscle, fat and bone marrow.
Clinical Physiology and Functional Imaging 11/2013; · 1.33 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: To assess the ability of co-occurrence matrix-based texture parameters to detect exercise load-associated differences in MRI texture at the femoral neck cross-section.
A total of 91 top-level female athletes representing five differently loading sports and 20 referents participated in this cross-sectional study. Axial T1-weighted FLASH and T2*-weighted MEDIC sequence images of the proximal femur were obtained with a 1.5T MRI. The femoral neck trabecular bone at the level of the insertion of articular capsule was divided manually into regions of interest representing four anatomical sectors (anterior, posterior, superior, and inferior). Selected co-occurrence matrix-based texture parameters were used to evaluate differences in apparent trabecular structure between the exercise loading groups and anatomical sectors of the femoral neck.
Significant differences in the trabecular bone texture, particularly at the superior femoral neck, were observed between athletes representing odd-impact (soccer and squash) and high-magnitude exercise loading (power-lifting) groups and the nonathletic reference group.
MRI texture analysis provides a quantitative method for detecting and classifying apparent structural differences in trabecular bone that are associated with specific exercise loading.
Journal of Magnetic Resonance Imaging 09/2011; 34(6):1359-66. · 2.57 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Early-stage diagnosis of Parkinson's disease (PD) is essential in making decisions related to treatment and prognosis. However, there is no specific diagnostic test for the diagnosis of PD. The aim of this study was to evaluate the role of texture analysis (TA) of magnetic resonance images in detecting subtle changes between the hemispheres in various brain structures in patients with early symptoms of parkinsonism. In addition, functional TA parameters for detecting textural changes are presented.
Fifty-one patients with symptoms of PD and 20 healthy controls were imaged using a 3-T magnetic resonance device. Co-occurrence matrix-based TA was applied to detect changes in textures between the hemispheres in the following clinically interesting areas: dentate nucleus, basilar pons, substantia nigra, globus pallidus, thalamus, putamen, caudate nucleus, corona radiata, and centrum semiovale. The TA results were statistically evaluated using the Mann-Whitney U test.
The results showed interhemispheric textural differences among the patients, especially in the area of basilar pons and midbrain. Concentrating on this clinically interesting area, the four most discriminant parameters were defined: co-occurrence matrix correlation, contrast, difference variance, and sum variance. With these parameters, differences were also detected in the dentate nucleus, globus pallidus, and corona radiata.
On the basis of this study, interhemispheric differences in the magnetic resonance images of patients with PD can be identified by the means of co-occurrence matrix-based TA. The detected areas correlate with the current pathophysiologic and neuroanatomic knowledge of PD.
[Show abstract][Hide abstract] ABSTRACT: The accuracy of texture analysis in clinical evaluation of magnetic resonance images depends considerably on imaging arrangements and various image quality parameters. In this paper, we study the effect of slice thickness on brain tissue texture analysis using a statistical approach and classification of T1-weighted images of clinically confirmed multiple sclerosis patients.
We averaged the intensities of three consecutive 1-mm slices to simulate 3-mm slices. Two hundred sixty-four texture parameters were calculated for both the original and the averaged slices. Wilcoxon's signed ranks test was used to find differences between the regions of interest representing white matter and multiple sclerosis plaques. Linear and nonlinear discriminant analyses were applied with several separate training and test sets to determine the actual classification accuracy.
Only moderate differences in distributions of the texture parameter value for 1-mm and simulated 3-mm-thick slices were found. Our study also showed that white matter areas are well separable from multiple sclerosis plaques even if the slice thickness differs between training and test sets.
Three-millimeter-thick magnetic resonance image slices acquired with a 1.5 T clinical magnetic resonance scanner seem to be sufficient for texture analysis of multiple sclerosis plaques and white matter tissue.
[Show abstract][Hide abstract] ABSTRACT: Magnetic resonance imaging (MRI)-based texture analysis has been shown to be effective in classifying multiple sclerosis lesions. Regarding the clinical use of texture analysis in multiple sclerosis, our intention was to show which parts of the analysis are sensitive to slight changes in textural data acquisition and which steps tolerate interference.
The MRI datasets of 38 multiple sclerosis patients were used in this study. Three imaging sequences were compared in quantitative analyses, including a comparison of anatomical levels of interest, variance between sequential slices and two methods of region of interest drawing. We focused on the classification of white matter and multiple sclerosis lesions in determining the discriminatory power of textural parameters. Analyses were run with MaZda software for texture analysis, and statistical tests were performed for raw parameters.
MRI texture analysis based on statistical, autoregressive-model and wavelet-derived texture parameters provided an excellent distinction between the image regions corresponding to multiple sclerosis plaques and white matter or normal-appearing white matter with high accuracy (nonlinear discriminant analysis 96%-100%). There were no significant differences in the classification results between imaging sequences or between anatomical levels. Standardized regions of interest were tolerant of changes within an anatomical level when intra-tissue variance was tested.
The MRI texture analysis protocol with fixed imaging sequence and anatomical levels of interest shows promise as a robust quantitative clinical means for evaluating multiple sclerosis lesions.
[Show abstract][Hide abstract] ABSTRACT: Magnetic resonance imaging based texture analysis has been shown effective on classifying multiple sclerosis lesions. Quantitative
analysis of images contains several manual and automatic steps. Results of subtle changes between tissues may suffer from
errors in analysis protocol. For the development of clinical analysis protocol we evaluate the potential of non-specialized
medics to carry out image slice selection and manual segmentation of brain tissue for texture analysis purposes as an assistant
for a specialist.
Our results indicate manual region of interest definition performed by non-specialists requires sufficient education with
practical training to achieve successful textural data.
KeywordsMagnetic resonance imaging (MRI)-texture analysis (TA)-multiple sclerosis (MS)-region of interest (ROI)-segmentation
[Show abstract][Hide abstract] ABSTRACT: Texture analysis (TA) is a potential tool for analysis of medical images. It can be used for classification of pathological
tissue. Co-occurrence matrix is one of the most promising TA methods. In this study we have analysed this method by using
software phantoms instead of physical phantoms. This choice was made because the construction process of a real phantom is
slow and only one set of parameter results is available. The software phantoms were implemented with Matlab program by constructing
16x16 matrices containing four different grey level values. Value from 0 to 1, 0 corresponding to black and 1 being white,
was given for each matrix element to perform different texture patterns. Grey scale images were drawn from the matrices and
the texture analysis was performed with MaZda. Software phantoms were proved to be an effective method to study the parameter
value distribution because of the easy construction and modification of the matrices. However, more complex patterns should
be used for further studies.
[Show abstract][Hide abstract] ABSTRACT: Texture analysis (TA) is a quantitative approach for characterizing subtle changes in magnetic resonance (MR) images of different
tissues. The aim of this study was to detect changes in tissue of corpus callosum (CC) in mild traumatic brain injury (MTBI)
patients by the means of TA.
TA was performed in the sagittal T1-weighted MR images of 42 MTBI patients, focusing on different segments of CC by using
the tissue characterization software MaZda. Results were compared with the control group of ten healthy volunteers. The most
discriminant texture features were identified with a combination of feature selection algorithms mutual information (MI),
classification error probability combined with average correlation coefficients (POE+ACC) and Fisher coefficient. Linear discriminant
analysis (LDA) and nonlinear discriminant analysis (NDA) were performed. Nearestneighbor (1-NN) classification for LDA and
artificial neural network (ANN) for NDA was used for tissue classification.
The results revealed differences in the textures between the selected segments of CC in MTBI patients. There were also differences
in the CC between healthy volunteers and MTBI patients. The best classification results between healthy volunteers and patients
were achieved in the area of splenium of CC, with accuracy of 96% for the 1-NN classifier, and accuracy of 98 % for the ANN
TA results revealed changes in the texture parameters of the segments of CC between healthy volunteers and MTBI patients and
therefore may provide a novel additional tool for detecting subtle changes in CC tissue on MTBI, but evidently larger data
is necessary to confirm the clinical value of TA in diagnosing MTBI.
[Show abstract][Hide abstract] ABSTRACT: To show magnetic resonance imaging (MRI) texture appearance change in non-Hodgkin lymphoma (NHL) during treatment with response controlled by quantitative volume analysis.
A total of 19 patients having NHL with an evaluable lymphoma lesion were scanned at three imaging timepoints with 1.5T device during clinical treatment evaluation. Texture characteristics of images were analyzed and classified with MaZda application and statistical tests.
NHL tissue MRI texture imaged before treatment and under chemotherapy was classified within several subgroups, showing best discrimination with 96% correct classification in non-linear discriminant analysis of T2-weighted images.Texture parameters of MRI data were successfully tested with statistical tests to assess the impact of the separability of the parameters in evaluating chemotherapy response in lymphoma tissue.
Texture characteristics of MRI data were classified successfully; this proved texture analysis to be potential quantitative means of representing lymphoma tissue changes during chemotherapy response monitoring.
Journal of Experimental & Clinical Cancer Research 07/2009; 28:87. · 3.27 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Breast cancer is the most common cancer in women. Breast MRI (BMRI) has emerged as a promising technique for detecting, diagnosing,
and staging the condition. Automated image analysis aims to extract relevant information from MR images of the breast and
improve the accuracy and consistency of image interpretation. Texture analysis (TA) is one possible means of detecting tissue
features in biomedical images.
The aim of this study was to evaluate the parameters which identify the most important breast cancer characteristics and to
assess the ability of MRI-based TA to characterize breast cancer tissue. Seven patients with histopathologically proven breast
cancer were included in this preliminary study. The texture analysis was performed with MaZda texture application. The most
discriminant texture features identified by Fisher coefficients and POE+ACC (probability of classification error and average
correlation coefficients) between breast cancer tissue and reference tissue from the healthy breast and tissue adjoining the
cancer area were evaluated. This evaluation was made between patients, different imaging series and two histological types
of (ductal vs. lobular) carcinomas. Raw data analysis (RDA), principal component analysis (PCA), linear discriminant analysis
(LDA) and nonlinear discriminant analysis (NDA) were run for each subset of images and chosen texture features. The results
revealed differences in the textures in every imaging series when non-cancer and cancer tissue were compared and the best
discrimination results were obtained within two dynamic contrast-enhanced MRI subtraction series. Furthermore, the texture
parameters obtained differed between the two histological groups. The preliminary results show potential in discriminating
between normal and abnormal breast tissue elements, encouraging us to continue with larger data sets.
[Show abstract][Hide abstract] ABSTRACT: Compared to high-impact exercises, moderate-magnitude impacts from odd-loading directions have similar ability to thicken vulnerable cortical regions of the femoral neck. Since odd-impact exercises are mechanically less demanding to the body, this type of exercise can provide a reasonable basis for devising feasible, targeted bone training against hip fragility.
Regional cortical thinning at the femoral neck is associated with hip fragility. Here, we investigated whether exercises involving high-magnitude impacts, moderate-magnitude impacts from odd directions, high-magnitude muscle forces, low-magnitude impacts at high repetition rate, or non-impact muscle forces at high repetition rate were associated with thicker femoral neck cortex.
Using three-dimensional magnetic resonance imaging, we scanned the proximal femur of 91 female athletes, representing the above-mentioned five exercise-loadings, and 20 referents. Cortical thickness at the inferior, anterior, superior, and posterior regions of the femoral neck was evaluated. Between-group differences were analyzed with ANCOVA.
For the inferior cortical thickness, only the high-impact group differed significantly (approximately 60%, p = 0.012) from the reference group, while for the anterior cortex, both the high-impact and odd-impact groups differed (approximately 20%, p = 0.042 and p = 0.044, respectively). Also, the posterior cortex was approximately 20% thicker (p = 0.014 and p = 0.006, respectively) in these two groups.
Odd-impact exercise-loading was associated, similar to high-impact exercise-loading, with approximately 20% thicker cortex around the femoral neck. Since odd-impact exercises are mechanically less demanding to the body than high-impact exercises, it is argued that this type of bone training would offer a feasible basis for targeted exercise-based prevention of hip fragility.
Osteoporosis International 12/2008; 20(8):1321-8. · 4.04 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The aim here is to show that texture parameters of magnetic resonance imaging (MRI) data changes in lymphoma tissue during chemotherapy. Ten patients having non-Hodgkin lymphoma masses in the abdomen were imaged for chemotherapy response evaluation three consecutive times. The analysis was performed with MaZda texture analysis (TA) application. The best discrimination in lymphoma MRI texture was obtained within T2-weighted images between the pre-treatment and the second response evaluation stage. TA proved to be a promising quantitative means of representing lymphoma tissue changes during medication follow-up.
Computers in Biology and Medicine 05/2008; 38(4):519-24. · 1.48 Impact Factor