The classification of bioimages plays an important role in several biological studies, such as subcellular localisation, phenotype identification and other types of histopathological examinations. The objective of the present study was to develop a computer-aided bioimage classification method for the classification of bioimages across nine diverse benchmark datasets. A novel algorithm was developed, which systematically fused the features extracted from nine different convolution neural network architectures. A systematic fusion of features boosts the performance of a classifier but at the cost of the high dimensionality of the fused feature set. Therefore, non-discriminatory and redundant features need to be removed from a high-dimensional fused feature set to improve the classification performance and reduce the time complexity. To achieve this aim, a method based on analysis of variance and evolutionary feature selection was developed to select an optimal set of discriminatory features from the fused feature set. The proposed method was evaluated on nine different benchmark datasets. The experimental results showed that the proposed method achieved superior performance, with a significant reduction in the dimensionality of the fused feature set for most bioimage datasets. The performance of the proposed feature selection method was better than that of some of the most recent and classical methods used for feature selection. Thus, the proposed method was desirable because of its superior performance and high compression ratio, which significantly reduced the computational complexity.

• Klíčová slova
• Bioimage classification, Convolutional neural networks, Evolutionary algorithms, Feature fusion, Pre-trained CNNs, Transfer learning,
• MeSH
• algoritmy * MeSH
• neuronové sítě * MeSH
• Publikační typ
• časopisecké články MeSH

Machine learning (ML) is a growing field that provides tools for automatic pattern recognition. The neuroimaging community currently tries to take advantage of ML in order to develop an auxiliary diagnostic tool for schizophrenia diagnostics. In this letter, we present a classification framework based on features extracted from magnetic resonance imaging (MRI) data using two automatic whole-brain morphometry methods: voxel-based (VBM) and deformation-based morphometry (DBM). The framework employs a random subspace ensemble-based artificial neural network classifier-in particular, a multilayer perceptron (MLP). The framework was tested on data from first-episode schizophrenia patients and healthy controls. The experiments differed in terms of feature extraction methods, using VBM, DBM, and a combination of both morphometry methods. Thus, features of different types were available for model adaptation. As we expected, the combination of features increased the MLP classification accuracy up to 73.12%-an improvement of 5% versus MLP-based only on VBM or DBM features. To further verify the findings, other comparisons using support vector machines in place of MLPs were made within the framework. However, it cannot be concluded that any classifier was better than another.

• MeSH
• diagnóza počítačová metody   MeSH
• lidé MeSH
• magnetická rezonanční tomografie * metody   MeSH
• mozek diagnostické zobrazování   MeSH
• neuronové sítě * MeSH
• rozpoznávání automatizované metody   MeSH
• schizofrenie diagnostické zobrazování   MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Rapid and reliable identification of insects is important in many contexts, from the detection of disease vectors and invasive species to the sorting of material from biodiversity inventories. Because of the shortage of adequate expertise, there has long been an interest in developing automated systems for this task. Previous attempts have been based on laborious and complex handcrafted extraction of image features, but in recent years it has been shown that sophisticated convolutional neural networks (CNNs) can learn to extract relevant features automatically, without human intervention. Unfortunately, reaching expert-level accuracy in CNN identifications requires substantial computational power and huge training data sets, which are often not available for taxonomic tasks. This can be addressed using feature transfer: a CNN that has been pretrained on a generic image classification task is exposed to the taxonomic images of interest, and information about its perception of those images is used in training a simpler, dedicated identification system. Here, we develop an effective method of CNN feature transfer, which achieves expert-level accuracy in taxonomic identification of insects with training sets of 100 images or less per category, depending on the nature of data set. Specifically, we extract rich representations of intermediate to high-level image features from the CNN architecture VGG16 pretrained on the ImageNet data set. This information is submitted to a linear support vector machine classifier, which is trained on the target problem. We tested the performance of our approach on two types of challenging taxonomic tasks: 1) identifying insects to higher groups when they are likely to belong to subgroups that have not been seen previously and 2) identifying visually similar species that are difficult to separate even for experts. For the first task, our approach reached $CDATA[$CDATA[$>$$92% accuracy on one data set (884 face images of 11 families of Diptera, all specimens representing unique species), and $CDATA[$CDATA[$>$$96% accuracy on another (2936 dorsal habitus images of 14 families of Coleoptera, over 90% of specimens belonging to unique species). For the second task, our approach outperformed a leading taxonomic expert on one data set (339 images of three species of the Coleoptera genus Oxythyrea; 97% accuracy), and both humans and traditional automated identification systems on another data set (3845 images of nine species of Plecoptera larvae; 98.6 % accuracy). Reanalyzing several biological image identification tasks studied in the recent literature, we show that our approach is broadly applicable and provides significant improvements over previous methods, whether based on dedicated CNNs, CNN feature transfer, or more traditional techniques. Thus, our method, which is easy to apply, can be highly successful in developing automated taxonomic identification systems even when training data sets are small and computational budgets limited. We conclude by briefly discussing some promising CNN-based research directions in morphological systematics opened up by the success of these techniques in providing accurate diagnostic tools.

• MeSH
• fylogeneze MeSH
• hmyz klasifikace   MeSH
• klasifikace metody   MeSH
• neuronové sítě * MeSH
• reprodukovatelnost výsledků MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• hodnotící studie MeSH
• práce podpořená grantem MeSH

The process of manual species identification is a daunting task, so much so that the number of taxonomists is seen to be declining. In order to assist taxonomists, many methods and algorithms have been proposed to develop semi-automated and fully automated systems for species identification. While semi-automated tools would require manual intervention by a domain expert, fully automated tools are assumed to be not as reliable as manual or semiautomated identification tools. Hence, in this study we investigate the accuracy of fully automated and semi-automated models for species identification. We have built fully automated and semi-automated species classification models using the monogenean species image dataset. With respect to monogeneans' morphology, they are differentiated based on the morphological characteristics of haptoral bars, anchors, marginal hooks and reproductive organs (male and female copulatory organs). Landmarks (in the semi-automated model) and shape morphometric features (in the fully automated model) were extracted from four monogenean species images, which were then classified using k-nearest neighbour and artificial neural network. In semi-automated models, a classification accuracy of 96.67 % was obtained using the k-nearest neighbour and 97.5 % using the artificial neural network, whereas in fully automated models, a classification accuracy of 90 % was obtained using the k-nearest neighbour and 98.8 % using the artificial neural network. As for the crossvalidation, semi-automated models performed at 91.2 %, whereas fully automated models performed slightly higher at 93.75 %.

• MeSH
• algoritmy MeSH
• automatizace MeSH
• druhová specificita MeSH
• počítačové zpracování obrazu metody   MeSH
• terminologie jako téma * MeSH
• Publikační typ
• časopisecké články MeSH
• srovnávací studie MeSH

Bioelectrical impedance analysis (BIA) was established to quantify diverse cellular characteristics. This technique has been widely used in various species, such as fish, poultry, and humans for compositional analysis. This technology was limited to offline quality assurance/detection of woody breast (WB); however, inline technology that can be retrofitted on the conveyor belt would be more helpful to processors. Freshly deboned (n = 80) chicken breast fillets were collected from a local processor and analyzed by hand-palpation for different WB severity levels. Data collected from both BIA setups were subjected to supervised and unsupervised learning algorithms. The modified BIA showed better detection ability for regular fillets than the probe BIA setup. In the plate BIA setup, fillets were 80.00% for normal, 66.67% for moderate (data for mild and moderate merged), and 85.00% for severe WB. However, hand-held BIA showed 77.78, 85.71, and 88.89% for normal, moderate, and severe WB, respectively. Plate BIA setup is more effective in detecting WB myopathies and could be installed without slowing the processing line. Breast fillet detection on the processing line can be significantly improved using a modified automated plate BIA.

• Klíčová slova
• bioelectrical impedance, hand palpation, in-line processing, supervised learning, unsupervised learning, woody breast,
• Publikační typ
• časopisecké články MeSH

Glaucoma is a disease of the retina which is one of the most common causes of permanent blindness worldwide. This paper presents an automatic image processing based method for glaucoma diagnosis from the digital fundus image. In this paper wavelet feature extraction has been followed by optimized genetic feature selection combined with several learning algorithms and various parameter settings. Unlike the existing research works where the features are considered from the complete fundus or a sub image of the fundus, this work is based on feature extraction from the segmented and blood vessel removed optic disc to improve the accuracy of identification. The experimental results presented in this paper indicate that the wavelet features of the segmented optic disc image are clinically more significant in comparison to features of the whole or sub fundus image in the detection of glaucoma from fundus image. Accuracy of glaucoma identification achieved in this work is 94.7% and a comparison with existing methods of glaucoma detection from fundus image indicates that the proposed approach has improved accuracy of classification.

• Klíčová slova
• Blood vessels, Classification, Feature extraction, Fundus image, Glaucoma, Wavelet transform,
• MeSH
• algoritmy * MeSH
• discus nervi optici patologie   MeSH
• dospělí MeSH
• glaukom patologie   MeSH
• interpretace obrazu počítačem metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• reprodukovatelnost výsledků MeSH
• retinoskopie metody   MeSH
• rozpoznávání automatizované metody   MeSH
• senioři MeSH
• senzitivita a specificita MeSH
• strojové učení MeSH
• subtrakční technika MeSH
• vlnková analýza MeSH
• vylepšení obrazu metody   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

This work presents an automated data-mining model for age-at-death estimation based on 3D scans of the auricular surface of the pelvic bone. The study is based on a multi-population sample of 688 individuals (males and females) originating from one Asian and five European identified osteological collections. Our method requires no expert knowledge and achieves similar accuracy compared to traditional subjective methods. Apart from data acquisition, the whole procedure of pre-processing, feature extraction and age estimation is fully automated and implemented as a computer program. This program is a part of a freely available web-based software tool called CoxAGE3D. This software tool is available at https://coxage3d.fit.cvut.cz/ Our age-at-death estimation method is suitable for use on individuals with known/unknown population affinity and provides moderate correlation between the estimated age and actual age (Pearson's correlation coefficient is 0.56), and a mean absolute error of 12.4 years.

• Klíčová slova
• 3D surface analysis, Adult age-at-death estimation, Auricular surface, Automated analysis, Data mining methods,
• MeSH
• data mining MeSH
• faciální stigmatizace MeSH
• lidé MeSH
• obličej MeSH
• pánevní kosti * diagnostické zobrazování   MeSH
• software MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Online coupling of Lab-In-Syringe automated headspace extraction to gas chromatography has been studied. The developed methodology was successfully applied to surface water analysis using benzene, toluene, ethylbenzene, and xylenes as model analytes. The extraction system consisted of an automatic syringe pump with a 5 mL syringe into which all solutions and air for headspace formation were aspirated. The syringe piston featured a longitudinal channel, which allowed connecting the syringe void directly to a gas chromatograph with flame ionization detector via a transfer capillary. Gas injection was achieved via opening a computer-controlled pinch valve and compressing the headspace, upon which separation was initialized. Extractions were performed at room temperature; yet sensitivity comparable to previous work was obtained by high headspace to sample ratio VHS/VSample of 1.6:1 and injection of about 77% of the headspace. Assistance by in-syringe magnetic stirring yielded an about threefold increase in extraction efficiency. Interferences were compensated by using chlorobenzene as an internal standard. Syringe cleaning and extraction lasting over 10 min was carried out in parallel to the chromatographic run enabling a time of analysis of <19 min. Excellent peak area repeatabilities with RSD of <4% when omitting and <2% RSD when using internal standard corrections on 100 μg L-1 level were achieved. An average recovery of 97.7% and limit of detection of 1-2 μg L-1 were obtained in analyses of surface water.

• Klíčová slova
• Btex, Gas chromatography – flame ionization detection, Headspace extraction, Magnetic-stirring Lab-In-Syringe, On-line coupling,
• MeSH
• automatizace MeSH
• benzen analýza   izolace a purifikace   MeSH
• benzenové deriváty analýza   izolace a purifikace   MeSH
• limita detekce MeSH
• mikroextrakce na pevné fázi MeSH
• plamínková ionizace metody   MeSH
• teplota MeSH
• toluen analýza   izolace a purifikace   MeSH
• voda chemie   MeSH
• xyleny analýza   izolace a purifikace   MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• benzen MeSH
• benzenové deriváty MeSH
• ethylbenzene MeSH Prohlížeč
• toluen MeSH
• voda MeSH
• xyleny MeSH

Microscopic image analysis plays a significant role in initial leukemia screening and its efficient diagnostics. Since the present conventional methodologies partly rely on manual examination, which is time consuming and depends greatly on the experience of domain experts, automated leukemia detection opens up new possibilities to minimize human intervention and provide more accurate clinical information. This paper proposes a novel approach based on conventional digital image processing techniques and machine learning algorithms to automatically identify acute lymphoblastic leukemia from peripheral blood smear images. To overcome the greatest challenges in the segmentation phase, we implemented extensive pre-processing and introduced a three-phase filtration algorithm to achieve the best segmentation results. Moreover, sixteen robust features were extracted from the images in the way that hematological experts do, which significantly increased the capability of the classifiers to recognize leukemic cells in microscopic images. To perform the classification, we applied two traditional machine learning classifiers, the artificial neural network and the support vector machine. Both methods reached a specificity of 95.31%, and the sensitivity of the support vector machine and artificial neural network reached 98.25 and 100%, respectively.

• Klíčová slova
• acute leukemia, automated leukemia detection, blood smear image analysis, cell segmentation, image processing, leukemic cell identification, machine learning,
• Publikační typ
• časopisecké články MeSH

Easy, efficient and low demanding separation of mRNA from biological material is needed to study gene expression and to use in chip technologies. It is common knowledge that each mRNA molecule contains sequence of 25 adenines. This feature can be used for binding mRNA on the surface of the particles coated by thymine chains. The present work reports on suggesting and optimizing of mRNA separation and detection from biological material via paramagnetic microparticles coupled with electrochemical detection. Primarily we optimized cyclic and square wave voltammetric conditions to detect poly(A), which was used as standard to mimic behaviour of mRNA. Under the optimized square wave voltammetric conditions (frequency 280 Hz, accumulation time 200 s, supporting electrolyte and its temperature: acetate buffer 4.6 and 35 degrees C) we estimated detection limit down to 1 ng of poly(A) per ml. To enhance effectiveness and repeatability of isolation of nucleic acid automated approach for rinsing and hybridizing was proposed. We optimized the whole procedure and experimental conditions. Using automated way of isolation and under optimized conditions the yield of poly(A) (isolated concentration of poly(A)/given concentration of poly(A)*100) was approximately 75%. The suggested and optimized method for poly(A) isolation and detection was utilized for the analysis of brain tissues of patients with traumatic brain injury. The total amount of isolated mRNA varied from 40 to 760 g of mRNA per g of brain tissue. The isolation of mRNA from six samples per run was not longer than 2.5h. Moreover, we applied the optimized procedure on fully automated pipetting instrument to isolate mRNA. The instrument was successfully tested on the analysis of extracts from roots of maize plants treated with cadmium(II) ions.

• MeSH
• adenin MeSH
• automatizace MeSH
• elektrochemické techniky metody   MeSH
• hybridizace nukleových kyselin MeSH
• kukuřice setá genetika   MeSH
• lidé MeSH
• magnetismus * MeSH
• messenger RNA izolace a purifikace   MeSH
• mozek - chemie MeSH
• nukleové kyseliny izolace a purifikace   MeSH
• párování bází MeSH
• poranění mozku genetika   MeSH
• thymin MeSH
• velikost částic MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• adenin MeSH
• messenger RNA MeSH
• nukleové kyseliny MeSH
• thymin MeSH