scholarly journals Image Segmentation by Image Foresting Transform with Boundary Polarity and Shape Constraints

2020 ◽  
Author(s):  
Lucy Mansilla ◽  
Paulo Miranda
Keyword(s):  
Image Segmentation ◽  
Target Object ◽  
Global Optimum ◽  
Theoretical Background ◽  
Boundary Constraints ◽  
Segmentation Methods ◽  
Image Foresting Transform ◽  
High Level ◽  
First Time ◽  
Multidimensional Images

Image segmentation, such as to extract an object from a background, is very useful for medical and biological image analysis. In this work, we propose new segmentation methods for interactive segmentation of multidimensional images, based on the Image Foresting Transform (IFT), by exploiting for the first time non-smooth connectivity functions (NSCF) with a strong theoretical background. The new algorithms provide global optimum solutions according to an energy function of graph cut, subject to high-level boundary constraints (polarity and shape). Our experimental results indicate substantial improvements in accuracy in relation to other state-of-the-art methods, using medical images by allowing the customization of the segmentation to a given target object.

scholarly journals Object segmentation by Oriented Image Foresting Transform with connectivity constraints

2019 ◽  
Author(s):  
Lucy A. C. Mansilla ◽  
Paulo A. V. Miranda
Keyword(s):  
Low Complexity ◽  
Target Object ◽  
Global Optimum ◽  
Global Properties ◽  
Simultaneous Control ◽  
Flow Algorithm ◽  
Np Hard Problem ◽  
Image Foresting Transform ◽  
High Level ◽  
Connectivity Constraint

Global properties, such as connectivity, shape constraints and boundary polarity, are useful high-level priors for image segmentation, allowing its customization for a given target object. In this work, we introduce a new method called Connected Oriented Image Foresting Transform (COIFT), which provides global optimum solutions according to a graph-cut measure, subject to the connectivity constraint in Oriented Image Foresting Transform (OIFT), ensuring the generation of connected objects, as well as allowing the simultaneous control of the boundary polarity. While the use of connectivity constraints in other frameworks, such as in the min-cut/max-flow algorithm, leads to an NP-Hard problem, COIFT conserves the low complexity of the OIFT algorithm. Experiments show that COIFT can considerably improve the segmentation of objects with thin and elongated parts, for the same number of seeds in segmentation based on markers.

scholarly journals Brain Image Segmentation in Recent Years: A Narrative Review

2021 ◽  
Vol 11 (8) ◽  
pp. 1055
Author(s):  
Ali Fawzi ◽  
Anusha Achuthan ◽  
Bahari Belaton
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Magnetic Resonance Images ◽  
Clinical Environment ◽  
Brain Image ◽  
Advantages And Disadvantages ◽  
Segmentation Methods ◽  
Drastic Increase ◽  
High Level ◽  
Brain Image Segmentation

Brain image segmentation is one of the most time-consuming and challenging procedures in a clinical environment. Recently, a drastic increase in the number of brain disorders has been noted. This has indirectly led to an increased demand for automated brain segmentation solutions to assist medical experts in early diagnosis and treatment interventions. This paper aims to present a critical review of the recent trend in segmentation and classification methods for brain magnetic resonance images. Various segmentation methods ranging from simple intensity-based to high-level segmentation approaches such as machine learning, metaheuristic, deep learning, and hybridization are included in the present review. Common issues, advantages, and disadvantages of brain image segmentation methods are also discussed to provide a better understanding of the strengths and limitations of existing methods. From this review, it is found that deep learning-based and hybrid-based metaheuristic approaches are more efficient for the reliable segmentation of brain tumors. However, these methods fall behind in terms of computation and memory complexity.

A Confrontation between Two Doctrines: The Birth of Struggle for Hegemony in Hebrew Children's Literature during the 1930s and 1940s

2008 ◽  
Vol 1 (2) ◽  
pp. 139-155 ◽  
Author(s):  
YAEL DARR
Keyword(s):  
Children's Literature ◽  
Educational System ◽  
Single Channel ◽  
Children’S Literature ◽  
The Political ◽  
Literary Creation ◽  
History Of ◽  
High Level ◽  
First Time ◽  
Literary Quality

This article describes a crucial and fundamental stage in the transformation of Hebrew children's literature, during the late 1930s and 1940s, from a single channel of expression to a multi-layered polyphony of models and voices. It claims that for the first time in the history of Hebrew children's literature there took place a doctrinal confrontation between two groups of taste-makers. The article outlines the pedagogical and ideological designs of traditionalist Zionist educators, and suggests how these were challenged by a group of prominent writers of adult poetry, members of the Modernist movement. These writers, it is argued, advocated autonomous literary creation, and insisted on a high level of literary quality. Their intervention not only dramatically changed the repertoire of Hebrew children's literature, but also the rules of literary discourse. The article suggests that, through the Modernists’ polemical efforts, Hebrew children's literature was able to free itself from its position as an apparatus controlled by the political-educational system and to become a dynamic and multi-layered field.

scholarly journals Aberrant Expression of TLR2, TLR7, TLR9, Splicing Variants of TLR4 and MYD88 in Chronic Lymphocytic Leukemia Patients

2021 ◽  
Vol 10 (4) ◽  
pp. 867
Author(s):  
Katarzyna Skorka ◽  
Paulina Wlasiuk ◽  
Agnieszka Karczmarczyk ◽  
Krzysztof Giannopoulos
Keyword(s):  
Bone Marrow ◽  
Chronic Lymphocytic Leukemia ◽  
Cytotoxic T Cells ◽  
Lymphocytic Leukemia ◽  
Aberrant Expression ◽  
Downstream Signaling ◽  
Splicing Variants ◽  
High Level ◽  
Time To First Treatment ◽  
First Time

Functional toll-like receptors (TLRs) could modulate anti-tumor effects by activating inflammatory cytokines and the cytotoxic T-cells response. However, excessive TLR expression could promote tumor progression, since TLR-induced inflammation might stimulate cancer cells expansion into the microenvironment. Myd88 is involved in activation NF-κB through TLRs downstream signaling, hence in the current study we provided, for the first time, a complex characterization of expression of TLR2, TLR4, TLR7, TLR9, and MYD88 as well as their splicing forms in two distinct compartments of the microenvironment of chronic lymphocytic leukemia (CLL): peripheral blood and bone marrow. We found correlations between MYD88 and TLRs expressions in both compartments, indicating their relevant cooperation in CLL. The MYD88 expression was higher in CLL patients compared to healthy volunteers (HVs) (0.1780 vs. 0.128, p < 0.0001). The TLRs expression was aberrant in CLL compared to HVs. Analysis of survival curves revealed a shorter time to first treatment in the group of patients with low level of TLR4(3) expression compared to high level of TLR4(3) expression in bone marrow (13 months vs. 48 months, p = 0.0207). We suggest that TLRs expression is differentially regulated in CLL but is similarly shared between two distinct compartments of the microenvironment.

scholarly journals PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Changyong Li ◽  
Yongxian Fan ◽  
Xiaodong Cai
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
State Of The Art ◽  
Experimental Results ◽  
Actual Situation ◽  
Controlled Experiments ◽  
Biomedical Image ◽  
Segmentation Methods ◽  
Art Performance

Abstract Background With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. Results A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

scholarly journals Revealing the constituents of Egypt’s oldest beer using infrared and mass spectrometry

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Mohamed A. Farag ◽  
Moamen M. Elmassry ◽  
Masahiro Baba ◽  
Renée Friedman
Keyword(s):  
Large Scale ◽  
Beer Brewing ◽  
Chemical Signatures ◽  
Chemical Determination ◽  
Materials Used ◽  
High Level ◽  
Beer Production ◽  
First Time ◽  
Detailed Chemical

Abstract Previous studies have shown that the Ancient Egyptians used malted wheat and barley as the main ingredients in beer brewing, but the chemical determination of the exact recipe is still lacking. To investigate the constituents of ancient beer, we conducted a detailed IR and GC-MS based metabolite analyses targeting volatile and non-volatile metabolites on the residues recovered from the interior of vats in what is currently the world’s oldest (c. 3600 BCE) installation for large-scale beer production located at the major pre-pharaonic political center at Hierakonpolis, Egypt. In addition to distinguishing the chemical signatures of various flavoring agents, such as dates, a significant result of our analysis is the finding, for the first time, of phosphoric acid in high level probably used as a preservative much like in modern beverages. This suggests that the early brewers had acquired the knowledge needed to efficiently produce and preserve large quantities of beer. This study provides the most detailed chemical profile of an ancient beer using modern spectrometric techniques and providing evidence for the likely starting materials used in beer brewing.

scholarly journals A NOVEL IMAGE SEGMENTATION APPROACH BASED ON NEUTROSOPHIC SET AND IMPROVED FUZZY C-MEANS ALGORITHM

2011 ◽  
Vol 07 (01) ◽  
pp. 155-171 ◽  
Author(s):  
H. D. CHENG ◽  
YANHUI GUO ◽  
YINGTAO ZHANG
Keyword(s):  
Image Segmentation ◽  
Point Of View ◽  
Neutrosophic Set ◽  
Convergence Criterion ◽  
Complex Objects ◽  
Fuzzy C Means ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Segmentation Approach ◽  
Fuzzy C Means Algorithm

Image segmentation is an important component in image processing, pattern recognition and computer vision. Many segmentation algorithms have been proposed. However, segmentation methods for both noisy and noise-free images have not been studied in much detail. Neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interaction with different ideational spectra. However, neutrosophic set needs to be specified and clarified from a technical point of view for a given application or field to demonstrate its usefulness. In this paper, we apply neutrosophic set and define some operations. Neutrosphic set is integrated with an improved fuzzy c-means method and employed for image segmentation. A new operation, α-mean operation, is proposed to reduce the set indeterminacy. An improved fuzzy c-means (IFCM) is proposed based on neutrosophic set. The computation of membership and the convergence criterion of clustering are redefined accordingly. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can segment images accurately and effectively. Especially, it can segment the clean images and the images having different gray levels and complex objects, which is the most difficult task for image segmentation.

A Survey on Solar Image Segmentation Techniques

2014 ◽  
Vol 945-949 ◽  
pp. 1899-1902
Author(s):  
Yuan Yuan Fan ◽  
Wei Jiang Li ◽  
Feng Wang
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Image Analysis ◽  
Image Segmentation ◽  
Image Retrieval ◽  
Fundamental Issue ◽  
Solar Image ◽  
Segmentation Methods

Image segmentation is one of the basic problems of image processing, also is the first essential and fundamental issue in the solar image analysis and pattern recognition. This paper summarizes systematically on the image segmentation techniques in the solar image retrieval and the recent applications of image segmentation. Then the merits and demerits of each method are discussed in this paper, in this way we can combine some methods for image segmentation to reach the better effects in astronomy. Finally, according to the characteristics of the solar image itself, the more appropriate image segmentation methods are summed up, and some remarks on the prospects and development of image segmentation are presented.

PREVENTION ISSUES OF OCCUPATIONAL AND PRODUCTION-RELATED DISEASES IN MINING INDUSTRY WORKERS

2021 ◽  
Author(s):  
G.S. Agzamova ◽  
◽  
N.U. Ibragimova ◽  
Yu.A. Abdieva ◽  
Keyword(s):  
Health Care ◽  
Metallurgical Plant ◽  
Occupational Diseases ◽  
Mining Industry ◽  
Production Environment ◽  
Cardiovascular Pathology ◽  
Dynamic Observation ◽  
High Level ◽  
Medical Examinations ◽  
First Time

Abstract: Protecting and promoting the health of workers in the mining industry is one of the most important problems of occupational pathology and health care. The structure and levels of prevention of occupational diseases are directly dependent on harmful and adverse factors of the production environment and labor process, adequately reflecting the state of production. Purpose: to study the issues of prevention of occupational and production-related diseases of mining and metallurgical plant workers. Research materials and methods: a dynamic observation of the health status of workers in the main industries of the mining and metallurgical plant (800 workers) was carried out. 92 patients with silicosis were examined. Results: Up to 92.8% of first-time occupational diseases are detected during periodic medical examinations. The prevailing sociomatic pathology is cardiovascular pathology, namely, arterial hypertension and diseases of the musculoskeletal system, mainly osteochondrosis of the spine. Prevalence of silicosis was observed in individuals with little professional experience (from 5 years old), young age and primary detection of patients in stage II silicosis, which was accompanied by respiratory failure. Conclusions: The prevention programme developed will ensure a high level of health care in terms of early diagnosis, rehabilitation and secondary prevention of both occupational and occupational diseases.

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