Medical image segmentation with transform and moment based features and incremental supervised neural network
In this study, a novel incremental supervised neural network (ISNN) is proposed for the segmentation of medical images. Performance of the ISNN is investigated for tissue segmentation in medical images obtained from various imaging modalities. Two feature extraction methods based on transform and moments are comparatively investigated to segment the tissues in medical images. Two-dimensional (2D) continuous wavelet transform (CWT) and the moments of the gray-level histogram (MGH) are computed in order to form the feature vectors of ultrasound (US) bladder and phantom images, X-ray computerized tomography (CT) and magnetic resonance (MR) head images. In the 2D-CWT method, feature vectors are formed by the intensity of one pixel of each wavelet-plane of different energy bands. The MGH represents the tissues within the sub-windows by using the spatial variation of image intensities. In this study, the ISNN and Grow and Learn (GAL) network are employed for the segmentation task. It is observed that the ISNN has significantly eliminated the disadvantages of the GAL network in the segmentation of the medical images.
In the internal medicine wide spectrum the gastroenterology is one of the chapters, less enlightened by the scientific evidence. It does not mean that the practice of the grasntroenterology may ot be improved by the systematic use of the approaches of the evidence based medicine
This book constitutes the proceedings of the 7th International Conference on Analysis of Images, Social Networks and Texts, AIST 2018, held in Moscow, Russia, in July 2018.
The 29 full papers were carefully reviewed and selected from 107 submissions (of which 26 papers were rejected without being reviewed). The papers are organized in topical sections on natural language processing; analysis of images and video; general topics of data analysis; analysis of dynamic behavior through event data; optimization problems on graphs and network structures; and innovative systems.
This open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation.
Soliton turbulence is studied within the framework of Gardner equation (generalized Korteweg-de Vries equation including quadratic and cubic nonlinear terms) by virtue of the direct numerical simulation of the ensemble dynamics. This equation allows the different soliton polarities to exist which make possible waves with extreme amplitudes to occur. Though the pairwise soliton collisions happen more frequently in the soliton gas, multiple soliton collisions have been identified as well involving up to five solitons. The emergence of abnormally large waves (rogue waves) of "unexpected" polarity is demonstrated. Different statistical properties of soliton turbulence (statistical moments, distribution functions) are analyzed. (C) 2019 Elsevier B.V. All rights reserved.
This research is dedicated to the design of a decision support system for categorization of scientific literature. The purpose of this work is to research possible ways to apply the machine learning algorithms to the automation of manual text categorization. The following stages are considered: preprocessing of raw data, word embedding, model selection, classification model, and software design. At the first stage, in collaboration with VINITI RAS, the training set of 200,000 Russian texts was formed. At the second stage, the word embedding model was justified as Word2Vec vector representation from text matrix by “sum” convolution with dimensionality 1500. At the third stage, the quality of the classifiers was estimated, and the logistic regression algorithm with the highest F1 score (0.94) was selected. And at the final stage, the ATC (Automatic Text Classifier) application, which embeds the results obtained on the previous stages, was developed. The overall application structure was described. It consists of compact program modules that can be replaced or adapted to the incoming text and gain the most classification score.
This paper presents an improved incremental self-organizing map (I2SOM) network that uses automatic threshold (AT) value for the segmentation of ultrasound (US) images. In order to show the validity of proposed scheme, it has been compared with Kohonen’s SOM. Two-dimensional (2D) fast Fourier transform (FFT) and 2D continuous wavelet transform (CWT) were computed in order to form the feature vectors of US bladder and phantom images. In this study, it is observed that the proposed automatic threshold scheme for ISOM network has significantly eliminated the former ISOM network’s threshold problem for US images. This improvement enhances the robustness of ISOM algorithm. Obtained results show that ISOM with AT value has similar segmentation performance with Kohonen’s network.
Data management and analysis is one of the fastest growing and most challenging areas of research and development in both academia and industry. Numerous types of applications and services have been studied and re-examined in this field resulting in this edited volume which includes chapters on effective approaches for dealing with the inherent complexity within data management and analysis. This edited volume contains practical case studies, and will appeal to students, researchers and professionals working in data management and analysis in the business, education, healthcare, and bioinformatics areas.
This prototype development explains the challenges encountered during the ISO/IEEE 11073 standard implementation process. The complexity of the standard and the consequent heavy requirements, which have not encouraged software engineers to adopt the standard. The developing complexity evaluation drives us to propose two possible implementation strategies that cover almost all possible use cases and eases handling the standard by non-expert users. The first one is focused on medical devices (MD) and proposes a low-memory and low-processor usage technique. It is based on message patterns that allow simple functions to generate ISO/IEEE 11073 messages and to process them easily. MD act as X73 agent. Second one is focused on more powerful device X73 manager, which do not have the MDs' memory and processor usage constraints. The protocol between Agent and Manager is point-to-point and we can distribute the functionality between devices.
Developed both implementation X73 Agent and Manager will cut developing time for applications based on ISO/EEE 11073.
This volume presents new results in the study and optimization of information transmission models in telecommunication networks using different approaches, mainly based on theiries of queueing systems and queueing networks .
The paper provides a number of proposed draft operational guidelines for technology measurement and includes a number of tentative technology definitions to be used for statistical purposes, principles for identification and classification of potentially growing technology areas, suggestions on the survey strategies and indicators. These are the key components of an internationally harmonized framework for collecting and interpreting technology data that would need to be further developed through a broader consultation process. A summary of definitions of technology already available in OECD manuals and the stocktaking results are provided in the Annex section.