A comprehensive review: Segmentation of MRI images—brain tumor

Segmentation of tumors in human brain aims to classify different abnormal tissues (necrotic core, edema, active cells) from normal tissues (cerebrospinal fluid, gray matter, white matter) of the brain. In existence, detection of abnormal tissues is easy for studying brain tumor, but reproducibility, characterization of abnormalities and accuracy are complicated in the process of segmentation. The magnetic resonance imaging (MRI)‐based segmentation of tumors in brain images is more enhancing and attracting in current years of research studies. It is due to non‐invasive examination and good contrast prone to soft tissues of images obtained from MRI modality. Medical approval of different segmentation techniques depends on the benchmark and simplicity of the method. This article incorporates both fully‐automatic and semi‐automatic methods for segmentation. The outlook study of this article is to provide the summary of most significant segmentation methods of tumors in brain using MRI. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 295–304, 2016     

Fully automatic method for segmentation of brain tumor from multimodal magnetic resonance images using wavelet transformation and clustering technique

Fully automatic brain tumor segmentation is one of the critical tasks in magnetic resonance imaging (MRI) images. This proposed work is aimed to develop an automatic method for brain tumor segmentation process by wavelet transformation and clustering technique. The proposed method using discrete wavelet transform (DWT) for pre‐ and post‐processing, fuzzy c‐means (FCM) for brain tissues segmentation. Initially, MRI images are preprocessed by DWT to sharpen the images and enhance the tumor region. It assists to quicken the FCM clustering technique and classified into four major classes: gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), and background (BG). Then check the abnormality detection using Fuzzy symmetric measure for GM, WM, and CSF classes. Finally, DWT method is applied in segmented abnormal region of images respectively and extracts the tumor portion. The proposed method used 30 multimodal MRI training datasets from BraTS2012 database. Several quantitative measures were calculated and compared with the existing. The proposed method yielded the mean value of similarity index as 0.73 for complete tumor, 0.53 for core tumor, and 0.35 for enhancing tumor. The proposed method gives better results than the existing challenging methods over the publicly available training dataset from MICCAI multimodal brain tumor segmentation challenge and a minimum processing time for tumor segmentation. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 305–314, 2016     

Gamma Knife treatment planning: MR brain tumor segmentation and volume measurement based on unsupervised Fuzzy C‐Means clustering

Nowadays, radiation treatment is beginning to intensively use MRI thanks to its greater ability to discriminate healthy and diseased soft‐tissues. Leksell Gamma Knife® is a radio‐surgical device, used to treat different brain lesions, which are often inaccessible for conventional surgery, such as benign or malignant tumors. Currently, the target to be treated with radiation therapy is contoured with slice‐by‐slice manual segmentation on MR datasets. This approach makes the segmentation procedure time consuming and operator‐dependent. The repeatability of the tumor boundary delineation may be ensured only by using automatic or semiautomatic methods, supporting clinicians in the treatment planning phase. This article proposes a semiautomatic segmentation method, based on the unsupervised Fuzzy C‐Means clustering algorithm. Our approach helps segment the target and automatically calculates the lesion volume. To evaluate the performance of the proposed approach, segmentation tests on 15 MR datasets were performed, using both area‐based and distance‐based metrics, obtaining the following average values: Similarity Index = 95.59%, Jaccard Index = 91.86%, Sensitivity = 97.39%, Specificity = 94.30%, Mean Absolute Distance = 0.246[pixels], Maximum Distance = 1.050[pixels], and Hausdorff Distance = 1.365[pixels]. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 213–225, 2015     

Brain tumor severity analysis using modified multi‐texton histogram and hybrid kernel SVM

Magnetic resonance image (MRI) segmentation refers to a process of assigning labels to set of pixels or multiple regions. It plays a major role in the field of biomedical applications as it is widely used by the radiologists to segment the medical images input into meaningful regions. In recent years, various brain tumor detection techniques are presented in the literature. In this article, we have developed an approach to brain tumor detection and severity analysis is done using the various measures. The proposed approach comprises of preprocessing, segmentation, feature extraction, and classification. In preprocessing steps, we need to perform skull stripping and then, anisotropic filtering is applied to make image suitable for extracting features. In feature extraction, we have modified the multi‐texton histogram (MTH) technique to improve the feature extraction. In the classification stage, the hybrid kernel is designed and applied to training of support vector machine to perform automatic detection of tumor region in MRI images. For comparison analysis, our proposed approach is compared with the existing works using K‐cross fold validation method. From the results, we can conclude that the modified multi‐texton histogram with non‐linear kernels has shown the accuracy of 86% but the MTH with non‐linear kernels shows the accuracy of 83.8%.     

Unsupervised tissue classification of brain MR images for voxel‐based morphometry analysis

In this article, a fully unsupervised method for brain tissue segmentation of T1‐weighted MRI 3D volumes is proposed. The method uses the Fuzzy C‐Means (FCM) clustering algorithm and a Fully Connected Cascade Neural Network (FCCNN) classifier. Traditional manual segmentation methods require neuro‐radiological expertise and significant time while semiautomatic methods depend on parameter's setup and trial‐and‐error methodologies that may lead to high intraoperator/interoperator variability. The proposed method selects the most useful MRI data according to FCM fuzziness values and trains the FCCNN to learn to classify brain’ tissues into White Matter, Gray Matter, and Cerebro‐Spinal Fluid in an unsupervised way. The method has been tested on the IBSR dataset, on the BrainWeb Phantom, on the BrainWeb SBD dataset, and on the real dataset “University of Palermo Policlinico Hospital” (UPPH), Italy. Sensitivity, Specificity, Dice and F‐Factor scores have been calculated on the IBSR and BrainWeb datasets segmented using the proposed method, the FCM algorithm, and two state‐of‐the‐art brain segmentation software packages (FSL and SPM) to prove the effectiveness of the proposed approach. A qualitative evaluation involving a group of five expert radiologists has been performed segmenting the real dataset using the proposed approach and the comparison algorithms. Finally, a usability analysis on the proposed method and reference methods has been carried out from the same group of expert radiologists. The achieved results show that the segmentations of the proposed method are comparable or better than the reference methods with a better usability and degree of acceptance.     

A rapid automatic brain tumor detection method for MRI images using modified minimum error thresholding technique

This proposed work is aimed to develop a rapid automatic method to detect the brain tumor from T2‐weighted MRI brain images using the principle of modified minimum error thresholding (MET) method. Initially, modified MET method is applied to produce well segmented and sub‐structural clarity for MRI brain images. Further, using FCM clustering the appearance of tumor area is refined. The obtained results are compared with corresponding ground truth images. The quantitative measures of results were compared with the results of those conventional methods using the metrics predictive accuracy (PA), dice coefficient (DC), and processing time. The PA and DC values of the proposed method attained maximum value and processing time is minimum while compared to conventional FCM and k‐means clustering techniques. This proposed method is more efficient and faster than the existing segmentation methods in detecting the tumor region from T2‐weighted MRI brain images. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 77–85, 2015     

Positron Emitting Magnetic Nanoconstructs for PET/MR Imaging

Hybrid PET/MRI scanners have the potential to provide fundamental molecular, cellular, and anatomic information essential for optimizing therapeutic and surgical interventions. However, their full utilization is currently limited by the lack of truly multi‐modal contrast agents capable of exploiting the strengths of each modality. Here, we report on the development of long‐circulating positron‐emitting magnetic nanoconstructs (PEM) designed to image solid tumors for combined PET/MRI. PEMs are synthesized by a modified nano‐precipitation method mixing poly(lactic‐co‐glycolic acid) (PLGA), lipids, and polyethylene glycol (PEG) chains with 5 nm iron oxide nanoparticles (USPIOs). PEM lipids are coupled with 1,4,7,10‐tetraazacyclododecane‐1,4,7,10‐tetraacetic acid (DOTA) and subsequently chelated to ⁶⁴Cu. PEMs show a diameter of 140 ± 7 nm and a transversal relaxivity r₂ of 265.0 ± 10.0 (mM × s)^?1, with a r₂/r₁ ratio of 123. Using a murine xenograft model bearing human breast cancer cell line (MDA‐MB‐231), intravenously administered PEMs progressively accumulate in tumors reaching a maximum of 3.5 ± 0.25% ID/g tumor at 20 h post‐injection. Correlation of PET and MRI signals revealed non‐uniform intratumoral distribution of PEMs with focal areas of accumulation at the tumor periphery. These long‐circulating PEMs with high transversal relaxivity and tumor accumulation may allow for detailed interrogation over multiple scales in a clinically relevant setting.     

A hybrid PET‐MRI: An integrated molecular‐genetic imaging system with HRRT‐PET and 7.0‐T MRI

A PET‐MRI fusion system is developed for molecular‐genetic imaging. The purpose of the system is to obtain images of the in‐vivo human brain using two high‐end imaging devices, an advanced PET and an ultrahigh‐field MRI. These are the HRRT‐PET, a high‐resolution research tomograph dedicated to brain imaging on the molecular level, and the 7.0‐T MRI, an ultrahigh field version used for morphological imaging. HRRT‐PET delivers high‐resolution molecular imaging with a resolution down to 2.5 mm FWHM, which is currently the highest spatial resolution available for the observation of the human brain's molecular activities, including enzymes and receptors, which are manipulated genetically, such as reporter genes and probes. The 7.0‐T MRI began to reveal submillimeter resolution images of the cortical as well as deep brain areas, down to 250 μm, which allows us to visualize the fine details of the cortical and brainstem areas, including the line of Gennari in the visual cortex and the corticospinal tracts in the pontine area. The current PET‐MRI fusion imaging system produces the highest quality images of molecular and genetic activities of the human brain in vivo. It is starting to provide many answers to the key questions about the neurological diseases. Some of these start providing answers to many cognitive neuroscience problems with clear molecular and genetic bases. There is great potential in the PET‐MRI for early diagnosis of cancers as well as other neurological diseases, which we were previously unable to diagnose, such as microscopic molecular changes that occur in Parkinson's and Alzheimer's diseases. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 17, 252–265, 2007     

Computer aided automated detection and classification of brain tumors using CANFIS classification method

The development of abnormal cells in human brain leads to the formation of tumors. This article proposes an efficient approach for brain tumor detection and segmentation using image fusion and co-active adaptive neuro fuzzy inference system (CANFIS) classification method. The brain MRI images are fused and the dual tree complex wavelet transform is applied on the fused image. Then, the statistical features, local ternary pattern features and gray level co-occurrence matrix features. These extracted features are classified using CANFIS classification approach for the classification of source brain MRI image into either normal or abnormal. Further, morphological operations are applied on the abnormal brain MRI image for segmenting the tumor regions. The proposed methodology is evaluated with respect to the performance metrics sensitivity, specificity, positive predictive value, negative predictive value, tumor segmentation accuracy with detection rate. The proposed image fusion based brain tumor detection and classification methodology stated in this article achieves 96.5% of average sensitivity, 97.7% of average specificity, 87.6% of positive predictive value, 96.6% of negative predictive value, and 98.8% of average accuracy.     

Enhancing level set brain tumor segmentation using fuzzy shape prior information and deep learning

Magnetic resonance imaging (MRI) brain tumor segmentation is a crucial task for clinical treatment. However, it is challenging owing to variations in type, size, and location of tumors. In addition, anatomical variation in individuals, intensity non-uniformity, and noises adversely affect brain tumor segmentation. To address these challenges, an automatic region-based brain tumor segmentation approach is presented in this paper which combines fuzzy shape prior term and deep learning. We define a new energy function in which an Adaptively Regularized Kernel-Based Fuzzy C-Means (ARKFCM) Clustering algorithm is utilized for inferring the shape of the tumor to be embedded into the level set method. In this way, some shortcomings of traditional level set methods such as contour leakage and shrinkage have been eliminated. Moreover, a fully automated method is achieved by using U-Net to obtain the initial contour, reducing sensitivity to initial contour selection. The proposed method is validated on the BraTS 2017 benchmark dataset for brain tumor segmentation. Average values of Dice, Jaccard, Sensitivity and specificity are 0.93 ± 0.03, 0.86 ± 0.06, 0.95 ± 0.04, and 0.99 ± 0.003, respectively. Experimental results indicate that the proposed method outperforms the other state-of-the-art methods in brain tumor segmentation.     

A fuzzy logic‐based meningioma tumor detection in magnetic resonance brain images using CANFIS and U‐Net CNN classification

This article develops a methodology for meningioma brain tumor detection process using fuzzy logic based enhancement and co‐active adaptive neuro fuzzy inference system and U‐Net convolutional neural network classification methods. The proposed meningioma tumor detection process consists of the following stages as, enhancement, feature extraction, and classifications. The enhancement of the source brain image is done using fuzzy logic and then dual tree‐complex wavelet transform is applied to this enhanced image at different levels of scale. The features are computed from the decomposed sub band images and these features are further classified using CANFIS classification method which identifies the meningioma brain image from nonmeningioma brain image. The performance of the proposed meningioma brain tumor detection and segmentation system is analyzed in terms of sensitivity, specificity, segmentation accuracy, and Dice coefficient index with detection rate.     

A fast and efficient computer aided diagnostic system to detect tumor from brain magnetic resonance imaging

In this work, a simple and efficient CAD (computer‐aided diagnostic) system is proposed for tumor detection from brain magnetic resonance imaging (MRI). Poor contrast MR images are preprocessed by using morphological operations and DSR (dynamic stochastic resonance) technique. The appropriate segmentation of MR images plays an important role in yielding the correct detection of tumor. On examination of three views of brain MRI, it was visible that the region of interest (ROI) lies in the middle and its size ranges from 240 × 240 mm² to 280 × 280 mm². The proposed system makes effective use of this information and identifies four blocks from the desired ROI through block‐based segmentation. Texture and shape features are extracted for each block of all MRIs in the training set. The range of these feature values defines the threshold to distinguish tumorous and nontumorous MRIs. Features of each block of an MRI view are checked against the threshold. For a particular feature, if a block is found tumorous in a view, then the other views are also checked for the presence of tumor. If corresponding blocks in all the views are found to be tumorous, then the MRI is classified as tumorous. This selective block processing technique improves computational efficiency of the system. The proposed technique is well adaptive and fast, and it is compared with well‐known existing techniques, like k‐means, fuzzy c‐means, etc. The performance analysis based on accuracy and precision parameters emphasizes the effectiveness and efficiency of the proposed work.     

Dotted Core–Shell Nanoparticles for T1‐Weighted MRI of Tumors

Gd‐based T ₁‐weighted contrast agents have dominated the magnetic resonance imaging (MRI) contrast agent market for decades. Nevertheless, they are reported to be nephrotoxic and the U.S. Food and Drug Administration has issued a general warning concerning their use. In order to reduce the risk of nephrotoxicity, the MRI performance of the Gd‐based T ₁‐weighted contrast agents needs to be improved to allow a much lower dosage. In this study, novel dotted core–shell nanoparticles (FeGd‐HN3‐RGD2) with superhigh r ₁ value (70.0 mM^?1 s^?1) and very low r ₂/r ₁ ratio (1.98) are developed for high‐contrast T ₁‐weighted MRI of tumors. 3‐(4,5‐Dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide (MTT) assay and histological analyses show good biocompatibility of FeGd‐HN3‐RGD2. Laser scanning confocal microscopy images and flow cytometry demonstrate active targeting to integrin α_vβ₃ positive tumors. MRI of tumors shows high tumor ΔSNR for FeGd‐HN3‐RGD2 (477 ± 44%), which is about 6‐7‐fold higher than that of Magnevist (75 ± 11%). MRI and inductively coupled plasma results further confirm that the accumulation of FeGd‐HN3‐RGD2 in tumors is higher than liver and spleen due to the RGD2 targeting and small hydrodynamic particle size (8.5 nm), and FeGd‐HN3‐RGD2 is readily cleared from the body by renal excretion.     

Exceedingly Small Gadolinium Oxide Nanoparticles with Remarkable Relaxivities for Magnetic Resonance Imaging of Tumors

Gd chelates have occupied most of the market of magnetic resonance imaging (MRI) contrast agents for decades. However, there have been some problems (nephrotoxicity, non‐specificity, and low r₁) that limit their applications. Herein, a wet‐chemical method is proposed for facile synthesis of poly(acrylic acid) (PAA) stabilized exceedingly small gadolinium oxide nanoparticles (ES‐GON‐PAA) with an excellent water dispersibility and a size smaller than 2.0 nm, which is a powerful T₁‐weighted MRI contrast agent for diagnosis of diseases due to its remarkable relaxivities (r₁ = 70.2 ± 1.8 mM^?1 s^?1, and r₂/r₁ = 1.02 ± 0.03, at 1.5 T). The r₁ is much higher and the r₂/r₁ is lower than that of the commercial Gd chelates and reported gadolinium oxide nanoparticles (GONs). Further ES‐GON‐PAA is developed with conjugation of RGD2 (RGD dimer) (i.e., ES‐GON‐PAA@RGD2) for T₁‐weighted MRI of tumors that overexpress RGD receptors (i.e., integrin α_vβ₃). The maximum signal enhancement (ΔSNR) for T₁‐weighted MRI of tumors reaches up to 372 ± 56% at 2 h post‐injection of ES‐GON‐PAA@RGD2, which is much higher than commercial Gd‐chelates (<80%). Due to the high biocompatibility and high tumor accumulation, ES‐GON‐PAA@RGD2 with remarkable relaxivities is a promising and powerful T₁‐weighted MRI contrast agent.     

In Vivo Tumor Visualization through MRI Off‐On Switching of NaGdF4–CaCO3 Nanoconjugates

The development of high‐performance contrast agents in magnetic resonance imaging (MRI) has recently received considerable attention, as they hold great promise and potential as a powerful tool for cancer diagnosis. Despite substantial achievements, it remains challenging to develop nanostructure‐based biocompatible platforms that can generate on‐demand MRI signals with high signal‐to‐noise ratios and good tumor specificity. Here, the design and synthesis of a new class of nanoparticle‐based contrast agents comprising self‐assembled NaGdF₄ and CaCO₃ nanoconjugates is reported. In this design, the spatial confinement of the T₁ source (Gd³⁺ ions) leads to an “OFF” MRI signal due to insufficient interaction between the protons and the crystal lattices. However, when immersed in the mildly acidic tumor microenvironment, the embedded CaCO₃ nanoparticles generate CO₂ bubbles and subsequently disconnect the nanoconjugate, thus resulting in an “ON” MRI signal. The in vivo performance of these nanoconjugates shows more than 60‐fold contrast enhancement in tumor visualization relative to the commercially used contrast agent Magnevist. This work presents a significant advance in the construction of smart MRI nanoprobes ideally suited for deep‐tissue imaging and target‐specific cancer diagnosis.     

A machine learning approach for brain image enhancement and segmentation

In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain's anatomical structures, for surgical planning, etc. However, due to presence of noise and uncertainty between different tissues in the brain image, the segmentation of brain is a challenging task. This problem is rectified in this article using two stages. In the first stage an enhancement technique called contrast limited fuzzy adaptive histogram equalization (CLFAHE) which is a combination of CLAHE and fuzzy enhancement is used to improve the contrast of MRI Brain images. Contrast of the image is controlled using contrast intensification operator (Clip limit). The second stage deals with the segmentation of enhanced image. The enhanced brain images are segmented using new level‐set method which has the property of both local and global segmentation. Signed pressure force (SPF) function is also used here which stops the contours at weak and blurred edged efficiently.     

Ten‐Gram‐Scale Facile Synthesis of Organogadolinium Complex Nanoparticles for Tumor Diagnosis

The market of available contrast agents for clinical magnetic resonance imaging (MRI) has been dominated by gadolinium (Gd) chelates based T₁ contrast agents for decades. However, there are growing concerns about their safety because they are retained in the body and are nephrotoxic, which necessitated a warning by the U.S. Food and Drug Administration against the use of such contrast agents. To ameliorate these problems, it is necessary to improve the MRI efficiency of such contrast agents to allow the administration of much reduced dosages. In this study, a ten‐gram‐scale facile method is developed to synthesize organogadolinium complex nanoparticles (i.e., reductive bovine serum albumin stabilized Gd‐salicylate nanoparticles, GdSalNPs‐rBSA) with high r₁ value of 19.51 mm ^?1 s^?1 and very low r₂/r₁ ratio of 1.21 (B₀ = 1.5 T) for high‐contrast T₁‐weighted MRI of tumors. The GdSalNPs‐rBSA nanoparticles possess more advantages including low synthesis cost (≈0.54 USD per g), long in vivo circulation time (t_1/2 = 6.13 h), almost no Gd³⁺ release, and excellent biosafety. Moreover, the GdSalNPs‐rBSA nanoparticles demonstrate excellent in vivo MRI contrast enhancement (signal‐to‐noise ratio (ΔSNR) ≈ 220%) for tumor diagnosis.     

Improved fuzzy entropy clustering algorithm for MRI brain image segmentation

Magnetic resonance imaging (MRI) brain image segmentation is essential at preliminary stage in the neuroscience research and computer‐aided diagnosis. However, presence of noise and intensity inhomogeneity in MRI brain images leads to improper segmentation. The fuzzy entropy clustering (FEC) is often used to deal with noisy data. One major disadvantage of the FEC algorithm is that it does not consider the local spatial information. In this article, we have proposed an improved fuzzy entropy clustering (IFEC) algorithm by introducing a new fuzzy factor, which incorporates both local spatial and gray‐level information. The IFEC algorithm is insensitive to noise, preserves the image detail during clustering, and is free of parameter selection. The efficacy of IFEC algorithm is demonstrated by comparing it quantitatively with the state‐of‐the‐art segmentation approaches in terms of similarity index on publically available real and simulated MRI brain images.     

A Highly‐Efficient Type I Photosensitizer with Robust Vascular‐Disruption Activity for Hypoxic‐and‐Metastatic Tumor Specific Photodynamic Therapy

Hypoxia severely impedes photodynamic therapy (PDT) efficiency. Worse still, considerable tumor metastasis will occur after PDT. Herein, an organic superoxide radical (O₂^??) nano‐photogenerator as a highly effcient type I photosensitizer with robust vascular‐disrupting efficiency to combat these thorny issues is designed. Boron difluoride dipyrromethene (BODIPY)‐vadimezan conjugate (BDPVDA) is synthesized and enwrapped in electron‐rich polymer‐brushes methoxy‐poly(ethylene glycol)‐b‐poly(2‐(diisopropylamino) ethyl methacrylate) (mPEG‐ PPDA) to afford nanosized hydrophilic type I photosensitizer (PBV NPs). Owing to outstanding core–shell intermolecular electron transfer between BDPVDA and mPEG‐PPDA, remarkable O₂^?? can be produced by PBV NPs under near‐infrared irradiation even in severe hypoxic environment (2% O₂), thus to accomplish effective hypoxic‐tumor elimination. Simultaneously, the efficient ester‐bond hydrolysis of BDPVDA in the acidic tumor microenvironment allows vadimezan release from PBV NPs to disrupt vasculature, facilitating the shut‐down of metastatic pathways. As a result, PBV NPs will not only be powerful in resolving the paradox between traditional type II PDT and hypoxia, but also successfully prevent tumor metastasis after type I PDT treatment (no secondary‐tumors found in 70 days and 100% survival rate), enabling enhancement of existing hypoxic‐and‐metastatic tumor treatment.     

Threshold prediction for segmenting tumour from brain MRI scans

In recent decades, region growing methods in image segmentation plays a vital role in medical image processing. Nonetheless, the method needs more advancement to cope up with the images of current acquisition devices. This paper attempts to solve the problem of maintaining diversity among wide image specifications by optimizing the threshold. In order to accomplish this, we introduce a hybrid framework of Artificial Bee Colony and Genetic Algorithm in a region growing variant, in which gradient and intensity levels are used for segmentation. Eventually, the proposed work is subjected to classify the tumor and non‐tumor images, followed by the segmentation of tumor region in MRI images. Classification methodologies such as feed forward back propagation neural network, radial basis neural network, support vector machine with quadratic programming and adaptive neuro‐fuzzy inference system are considered for experimental investigation in which support vector machine with quadratic programming is found to be dominant than other methodologies. Proposed region growing method outperforms well on the classified image, when compared with the region growing variant and standard region growing method. The results are demonstrated with the aid of wide set of performance measures. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 129–137, 2014