Automatic Morphological Analysis Research Articles

Deep learning-based cerebral aneurysm segmentation and morphological analysis with three-dimensional rotational angiography

BackgroundThe morphological assessment of cerebral aneurysms based on cerebral angiography is an essential step when planning strategy and device selection in endovascular treatment, but manual evaluation by human raters only...

DETERMINING THE MORPHOLOGICAL CLASS OF A WORD DURING THE AUTOMATIC NATURAL LANGUAGE PROCESSING

The article considers a mandatory component of the linguistic provision of any system of automatic natural language processing, i.e. automatic morphological analysis, the tasks of which include: determining for each text unit its place in the morphological system of the corresponding language; identification of word forms of the lexeme. As a result of automatic morphological analysis, each word form of the text is assigned a tag for the part of speech and the meaning of the grammatical categories (gender, number, case, aspect, tense, person, etc.). The nature of this information, its volume, and the methods used to establish morphological information depend on the purpose of the research, within which automatic analysis is carried out with the focus on the nature of the analyzed texts. Morphological analysis is present at all stages of text analysis, because neither morphemic, nor syntactic, nor semantic analysis can be performed without parts-of-speech tagging. With automatic syntactic analysis, only if lexical-grammatical and grammatical information is available for each word form, it is possible to syntactically bind word forms in a sentence. Morphological features of text units further become a tool for researching the relationship between vocabulary and grammar and the use in speech; between paradigmatics (in the aspect of consideration of case forms of declinable words) and syntagmatics (in the aspect of linear relationships of words, text coherence).
 The article examines the difficulties that prevent unifying the process of tagging text units, namely lexical-grammatical homonymy, ambiguity of grammatical forms, polysemy. The study considers approaches to resolving morphological ambiguity based on the context analysis of the ambiguous word, which can be divided into statistical and rule-based. Rules can be compiled manually or derived from marked-up corpora. Statistical methods are based on quantitative indicators in large labelled corpora. Morphological ambiguity resolution methods are usually applied after primary tagging, which is usually done using dictionaries.
 The article also provides a morphemic analysis algorithm for automatic morphological analysis.

Deep learning–based segmentation and quantification of podocyte foot process morphology suggests differential patterns of foot process effacement across kidney pathologies

Morphological alterations at the kidney filtration barrier increase intrinsic capillary wall permeability resulting in albuminuria. However, automated, quantitative assessment of these morphological changes has not been possible with electron or light microscopy. Here we present a deep learning-based approach for segmentation and quantitative analysis of foot processes in images acquired with confocal and super-resolution fluorescence microscopy. Our method, Automatic Morphological Analysis of Podocytes (AMAP), accurately segments podocyte foot processes and quantifies their morphology. AMAP applied to a set of kidney diseases in patient biopsies and a mouse model of focal segmental glomerulosclerosis allowed for accurate and comprehensive quantification of various morphometric features. With the use of AMAP, detailed morphology of podocyte foot process effacement was found to differ between categories of kidney pathologies, showed detailed variability between diverse patients with the same clinical diagnosis, and correlated with levels of proteinuria. AMAP could potentially complement other readouts such as various omics, standard histologic/electron microscopy and blood/urine assays for future personalized diagnosis and treatment of kidney disease. Thus, our novel finding could have implications to afford an understanding of early phases of kidney disease progression and may provide supplemental information in precision diagnostics.

Kladenští type as a problem of automatic morphological analysis

Abstract The aim of our paper is to demonstrate the procedures by which the data needed to refine tools for automatic morphological analysis of Czech can be obtained using a corpus, namely the Araneum Bohemicum IV Maximum (Czech, 20.03) 7.10 G web corpus of the ARANEA series and Araneum Bohemicum Maximum (Czech, 15.04) 3,20 G (hereinafter Araneum). Particularly, we will focus on propria of the Kladenští type, i.e., substantivized adjectives of denoting groups of persons according to affiliation. The goal of the probe into the Aranea web corpus is: 1) a corpus-based description of frequented properties of the Kladenští type, which can be used as a starting point for rule disambiguation; 2) creating a list of the most frequent lemmas belonging to the Kladenští type, which can then be included into dictionaries of automatic morphological analyzers (e.g. the MorfFlex dictionary by Hajič and Hlaváčová). We believe that the probe can help improve the results of tools for automatic morphological analysis of Czech.

A Real-Time Multifunctional Framework for Guidewire Morphological and Positional Analysis in Interventional X-Ray Fluoroscopy

In endovascular and cardiovascular surgery, real-time guidewire morphological and positional analysis is an important prerequisite for robot-assisted intervention, which can aid in reducing the radiation dose, contrast agent, and procedure time. Nevertheless, this task often comes with the challenge of the deformable elongated structure with low contrast in noisy X-ray fluoroscopy. In this article, a real-time multifunctional framework is proposed for fully automatic guidewire morphological and positional analysis, namely, guidewire segmentation, endpoint localization, and angle measurement. In the first stage, the proposed fast attention recurrent network (FAR-Net) achieves real-time and accurate guidewire segmentation. In the second stage, the endpoint localization and angle measurement algorithm robustly obtain subpixel-level endpoint and angle of the guidewire tip. Quantitative and qualitative evaluations on the MSGSeg data set consisting of 180 X-ray sequences from 30 patients demonstrate that the proposed framework significantly outperforms simpler baselines as well as the best previously published result for this task. The proposed approach reached F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -Score of 0.938, mean distance error of 0.596 pixels, endpoint localization and angle measurement accuracy of 97.8% and 95.3%, and an inference rate of approximately 13 FPS. The proposed framework not only addresses the issues of extreme class imbalance and misclassified examples but also meets the real-time requirements, achieving state-of-the-art performance. The proposed approach is promising for integration into robotic navigation frameworks to various intravascular applications, enabling robotic-assisted intervention.

Моделирование морфологического анализа и синтеза словоформ естественного языка

This article discusses the modeling of morphological analysis and synthesis of the Kyrgyz language. Models of creating word forms and rules for connecting affixes to the base of a word are considered. As a result of studying the structure of natural language, a algorithm of module was created that performs automatic morphological analysis and synthesis of word forms on a personal computer.

Выявление значимых признаков противоправных текстов

The purpose of the study: development of a technique for determining lexical characteristics and psycholinguistic factors as discriminative features for identifying the topics of illegal texts by frequency methods for information security purposes. Method: automatic morphological and syntactic analysis, frequency methods, comparison of auto-generated dictionaries by correlation analysis methods. Results: a technique of frequency analysis of the illegal texts vocabulary has been developed, which allows to compare different sets of texts using frequency dictionaries and identify discriminative features; a technique of calculating pairwise rank correlation coefficient for comparison of frequency dictionaries of various lexical characteristics has been presented; a comparative analysis of different illegal texts collections has been carried out; the possibility of using frequency lexical characteristics to study the properties of texts in order to detect illegal resources and messages has been shown; the possibilities of using both morphological characteristics of words and word combinations and letter combinations as discriminative features have been shown; the possibility of calculating the psycholinguistic indicators of illegal texts based on automatic linguistic text analysis has been shown; the psycholinguistic characteristics for texts of various topics have been highlighted.

Parts of Speech in NovaMorf, A New Morphological Annotation of Czech

Abstract A detailed morphological description of word forms in any language is a necessary condition for a successful automatic processing of linguistic data. The paper focuses on a new description of morphological categories, mainly on the subcategorization of parts of speech in Czech within the NovaMorf project. NovaMorf focuses on the description of morphological properties of Czech word forms in a more compact and consistent way and with a higher explicative power than approaches used so far. It also aims at the unification of diverse approaches to morphological annotation of Czech. NovaMorf approach will be reflected in a new morphological dictionary to be exploited for a new automatic morphological analysis (and disambiguation) of corpora of contemporary Czech.

Improving Nominalized Adjectives Tagging

Abstract Part of speech transitions represent an interesting issue in terms of Automatic Morphological Analysis (AMA). In these cases, two parts of speech have to be considered: initial and final. However, their automatic recognition is complicated by the same form. This article presents the results of a corpus study aimed at mapping nominalized adjectives tagging with a focus on detecting candidates for nominalization among frequent adjectives. Analysis of the data obtained from the ČNK SYN v5 corpus shows different reasons for incorrect tagging. Taking into account these reasons, we propose three solutions for the improvement nominalized adjectives tagging.

A Framework for Automatic Morphological Feature Extraction and Analysis of Abdominal Organs in MRI Volumes

The accurate 3D reconstruction of organs from radiological scans is an essential tool in computer-aided diagnosis (CADx) and plays a critical role in clinical, biomedical and forensic science research. The structure and shape of the organ, combined with morphological measurements such as volume and curvature, can provide significant guidance towards establishing progression or severity of a condition, and thus support improved diagnosis and therapy planning. Furthermore, the classification and stratification of organ abnormalities aim to explore and investigate organ deformations following injury, trauma and illness. This paper presents a framework for automatic morphological feature extraction in computer-aided 3D organ reconstructions following organ segmentation in 3D radiological scans. Two different magnetic resonance imaging (MRI) datasets are evaluated. Using the MRI scans of 85 adult volunteers, the overall mean volume for the pancreas organ is 69.30 ± 32.50cm3, and the 3D global curvature is (35.23 ± 6.83) × 10−3. Another experiment evaluates the MRI scans of 30 volunteers, and achieves mean liver volume of 1547.48 ± 204.19cm3 and 3D global curvature (19.87 ± 3.62) × 10− 3. Both experiments highlight a negative correlation between 3D curvature and volume with a statistical difference (p < 0.0001). Such a tool can support the investigation into organ related conditions such as obesity, type 2 diabetes mellitus and liver disease.

Automatic Morphological Analysis for Russian: Application-Oriented Survey

Automatic Morphological Analysis for Russian: Application-Oriented Survey

Corpus Analysis of Psychiatric Disorders Utilizing Natural Language Processing and Neurolinguistics

Natural language processing (NLP) is a technology in which a computer processes human "natural language" directly. Along with the development of technologies such as automatic morphological analysis and transition words or sentences to vectors, more research that targets psychiatric disorders utilizing NLP is being conducted. In this manuscript, we will review some of these studies. Such approaches may resolve many problems regarding the assessment of psychiatric illnesses, and are expected to contribute to neurolinguistics.

Compound Adverbs as an Issue in Machine Analysis of Czech language

Abstract Compound adverbs represent an interesting issue in terms of Automatic Morphological Analysis (AMA). The reason is that compound adverbs in Czech are expressions formed by compounding existing words that are different parts of speech without any change in their form. An indicative sign of compound adverbs is that they can always be decomposed again. Compound adverbs may be written as one word but sometimes a multiword form coexists. A word that is originally a different part of speech gains an adverbial meaning and becomes an adverb. This article presents the results of a corpus probe aimed at mapping expressions that are demonstrably compound adverbs and were not recognized by AMA or were incorrectly tagged by AMA as another part of speech. Analysis of data obtained from the Czech National Corpus (ČNK) SYN v3 show that the unrecognized and incorrectly tagged units can be divided into several groups. Based on knowledge of these groups it is possible to refine part of speech tagging by AMA. The corpus probe examined units written in accordance with the current codification as well as substandard units.

NLP MODULE FOR BULGARIAN TEXT PROCESSING

The wide use of web-based information systems and a lack of highly skilled developers are the primary motivation to search for methods and approaches to optimize the building of such systems. This paper describes a model for creating web-based information systems by using a core of reusable, independent, and installable base modules. Such a system is easily adapted to a client’s needs and is extendable by adding specific modules that interact with the remainder of the system by following certain rules. The approach allows flexible and rapid development of applications for small to extremely large web-based systems, simply by adding modules with adequate functionality. The growing demand of Bulgarian customers for such systems is the reason for building a base module for automatic processing of Bulgarian text. This paper presents a module that performs automatic morphological analysis and synthesis, verifies syntactic agreement, automatically places stress, and processes complex verb forms, among other functions. The described functionality can be integrated with other modules using a suitable interface

The Development of a Reading Support Tool for the Korean Language Using Automatic Morphological Analysis Technology

The Development of a Reading Support Tool for the Korean Language Using Automatic Morphological Analysis Technology

Cell Image Processing Methods for Automatic Cell Pattern Recognition and Morphological Analysis of Mesenchymal Stem Cells - An Algorithm for Cell Classification and Adaptive Brightness Correction -

Purpose: The present study aimed at image processing methods for automatic cell pattern recognition and morphological analysis for tissue engineering applications. The primary aim was to ascertain the novel algorithm of adaptive brightness correction from microscopic images for use as a potential image analysis. Methods: General microscopic image of cells has a minor problem which the central area is brighter than edge-area because of the light source. This may affect serious problems to threshold process for cell-number counting or cell pattern recognition. In order to compensate the problem, we processed to find the central point of brightness and give less weight-value as the distance to centroid. Results: The results presented that microscopic images through the brightness correction were performed clearer than those without brightness compensation. And the classification of mixed cells was performed as well, which is expected to be completed with pattern recognition later. Beside each detection ratio of hBMSCs and HeLa cells was 95% and 92%, respectively. Conclusions: Using this novel algorithm of adaptive brightness correction could control the easier approach to cell pattern recognition and counting cell numbers.

NEXUS: tracing the cosmic web connection

We introduce the NEXUS algorithm for the identification of Cosmic Web environments: clusters, filaments, walls and voids. This is a multiscale and automatic morphological analysis tool that identifies all the cosmic structures in a scale free way, without preference for a certain size or shape. We develop the NEXUS method to incorporate the density, tidal field, velocity divergence and velocity shear as tracers of the Cosmic Web. We also present the NEXUS+ procedure which, taking advantage of a novel filtering of the density in logarithmic space, is very successful at identifying the filament and wall environments in a robust and natural way. To asses the algorithms we apply them to an N-body simulation. We find that all methods correctly identify the most prominent filaments and walls, while there are differences in the detection of the more tenuous structures. In general, the structures traced by the density and tidal fields are clumpier and more rugged than those present in the velocity divergence and velocity shear fields. We find that the NEXUS+ method captures much better the filamentary and wall networks and is successful in detecting even the fainter structures. We also confirm the efficiency of our methods by examining the dark matter particle and halo distributions.

An automatic algorithm for the segmentation and morphological analysis of microvessels in immunostained histological tumour sections

A fully automatic segmentation and morphological analysis algorithm for the analysis of microvessels from CD31 immunostained histological tumour sections is presented. Development of the algorithm exploited the distinctive hues of stained vascular endothelial cells, cell nuclei and background, to provide the seeds for a 'region-growing' method for object segmentation in the 3D hue, saturation, value (HSV) colour model. The segmented objects, identified as microvessels by CD31 immunostaining, were post-processed with three morphological tasks: joining separate objects that were likely to belong to a single vessel, closing objects that had a narrow gap around their periphery, and splitting objects with multiple lumina into individual vessels. The automatic segmentation was validated against a hand-segmented set of 44 images from three different SW1222 human colorectal carcinomas xenografted into mice. 96.3 ± 0.9% of pixels were found to be correctly classified. Automated segmentation was carried out on a further 53 images from three histologically distinct mouse fibrosarcomas (MFs) for morphological comparison with the SW1222 tumours. Four morphometric measurements were calculated for each segmented vessel: vascular area (VA), ratio of lumen area to vascular area (lu/VA), eccentricity (e), and roundness (ro). In addition, the total vascular area relative to tumour tissue area (rVA) was calculated. lu/VA, e and ro were found to be significantly smaller in MF tumours than in SW1222 tumours (p < 0.05; unpaired t-test). The algorithm is available through the website http://www.caiman.org.uk where images can be uploaded, processed and sent back to users. The output from CAIMAN consists of the original image with boundaries of segmented vessels overlaid, the calculated parameters and a Matlab file, which contains the segmentation that the user can use to derive further results.

Longitudinal characterization of brain atrophy of a Huntington's disease mouse model by automated morphological analyses of magnetic resonance images

Mouse models of human diseases play crucial roles in understanding disease mechanisms and developing therapeutic measures. Huntington's disease (HD) is characterized by striatal atrophy that begins long before the onset of motor symptoms. In symptomatic HD, striatal volumes decline predictably with disease course. Thus, imaging based volumetric measures have been proposed as outcomes for presymptomatic as well as symptomatic clinical trials of HD. Magnetic resonance imaging of the mouse brain structures is becoming widely available and has been proposed as one of the biomarkers of disease progression and drug efficacy testing. However, three-dimensional and quantitative morphological analyses of the brains are not straightforward. In this paper, we describe a tool for automated segmentation and voxel-based morphological analyses of the mouse brains. This tool was applied to a well-established mouse model of Huntington's disease, the R6/2 transgenic mouse strain. Comparison between the automated and manual segmentation results showed excellent agreement in most brain regions. The automated method was able to sensitively detect atrophy as early as 4 weeks of age and accurately follow disease progression. Comparison between ex vivo and in vivo MRI suggests that the ex vivo end-point measurement of brain morphology is also a valid approach except for the morphology of the ventricles. This is the first report of longitudinal characterization of brain atrophy in a mouse model of Huntington's disease by using automatic morphological analysis.

Ambiguïté des kanji et stratégie de désambiguïsation par le contexte

In this article we will examine the ambiguity of kanji, characters of Chinese origin, and one of the character sets used to write the Japanese language. We will first describe the ambiguity phenomenon by presenting the origin of this problem and its multilevel effects on the Japanese texts decoding. Then, we will expose a context based method of disambiguating kanji which draws on Japanese graphotax specificities themselves. Lastly, we will show the effectiveness of this disambiguating method when applied to computational linguistics and particularly to an automatic morphological analysis and segmentation system for Japanese texts.