scholarly journals Automated Techniques for the Interpretation of Fetal Abnormalities: A Review

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Vidhi Rawat ◽  
Alok Jain ◽  
Vibhakar Shrimali
Keyword(s):  
Image Segmentation ◽  
Pregnant Woman ◽  
High Risk ◽  
Continuous Monitoring ◽  
Research Effort ◽  
Nuchal Translucency ◽  
Abnormality Detection ◽  
Fetal Abnormality ◽  
Biometric Parameters ◽  
Segmentation Methods

Ultrasound (US) image segmentation methods, focusing on techniques developed for fetal biometric parameters and nuchal translucency, are briefly reviewed. Ultrasound medical images can easily identify the fetus using segmentation techniques and calculate fetal parameters. It can timely find the fetal abnormality so that necessary action can be taken by the pregnant woman. Firstly, a detailed literature has been offered on fetal biometric parameters and nuchal translucency to highlight the investigation approaches with a degree of validation in diverse clinical domains. Then, a categorization of the bibliographic assessment of recent research effort in the segmentation field of ultrasound 2D fetal images has been presented. The fetal images of high-risk pregnant women have been taken into the routine and continuous monitoring of fetal parameters. These parameters are used for detection of fetal weight, fetal growth, gestational age, and any possible abnormality detection.

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 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.

Helping Analysts Deal with Data Overload: Profiling Profilers

2005 ◽  
Vol 49 (10) ◽  
pp. 908-912 ◽  
Author(s):  
Cynthia Dominguez ◽  
Donald Cox ◽  
William G. Long ◽  
Brian Moon ◽  
Gary Klein
Keyword(s):  
Information Retrieval ◽  
High Risk ◽  
National Security ◽  
Research Effort ◽  
Information Collection ◽  
Military Leaders ◽  
Cognitive Work ◽  
Data Overload ◽  
Current Lines ◽  
Time Critical

Intelligence analysts provide an incredibly important service toward maintaining our national security. They are chartered with providing the knowledge basis for decisions our national and military leaders make. Analysts must be depended upon to influence high-risk, time-critical, sometimes life-or-death decisions; thus, supporting their requirements is a vital goal for research and development efforts. In this paper, we focus on the cognitive aspects of analysts' information collection and filtering tasks, particularly of all-source analysts who continuously monitor message traffic. These analysts create synthesized reports for customers. We will discuss the issue of data overload and its effects, along with current lines of research on information retrieval. We end with a summary of our research effort examining filtering/profiling, to include approach and initial findings, towards our eventual goal of developing design seeds that can be incorporated into software tools to better support analysts' cognitive work.

Empirical Evaluation of Generic Weighted Cubicle Pattern and LBP Derivatives for Abnormality Detection in Mammogram Images

2015 ◽  
Vol 15 (01) ◽  
pp. 1550001 ◽  
Author(s):  
A. Suruliandi ◽  
G. Murugeswari ◽  
P. Arockia Jansi Rani
Keyword(s):  
Image Segmentation ◽  
Performance Metrics ◽  
Empirical Evaluation ◽  
Rate Sensitivity ◽  
Digital Mammogram ◽  
Abnormality Detection ◽  
Texture Descriptor ◽  
Texture Pattern ◽  
Local Texture ◽  
Mammogram Images

Digital image processing techniques are very useful in abnormality detection in digital mammogram images. Nowadays, texture-based image segmentation of digital mammogram images is very popular due to its better accuracy and precision. Local binary pattern (LBP) descriptor has attracted many researchers working in the field of texture analysis of digital images. Because of its success, many texture descriptors have been introduced as variants of LBP. In this work, we propose a novel texture descriptor called generic weighted cubicle pattern (GWCP) and we analyzed the proposed operator for texture image classification. We also performed abnormality detection through mammogram image segmentation using k-Nearest Neighbors (KNN) algorithm and compared the performance of the proposed texture descriptor with LBP and other variants of LBP namely local ternary pattern (LTPT), extended local texture pattern (ELTP) and local texture pattern (LTPS). For evaluation, we used the performance metrics such as accuracy, error rate, sensitivity, specificity, under estimation fraction and over estimation fraction. The results prove that the proposed method outperforms other descriptors in terms of abnormality detection in mammogram images.

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.

Image Segmentation Using Rough Set Theory

2014 ◽  
Vol 1 (2) ◽  
pp. 62-74 ◽  
Author(s):  
Payel Roy ◽  
Srijan Goswami ◽  
Sayan Chakraborty ◽  
Ahmad Taher Azar ◽  
Nilanjan Dey
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Gray Level ◽  
Rule Generation ◽  
Local Minima ◽  
Fuzzy Correlation ◽  
Segmentation Methods

In the domain of image processing, image segmentation has become one of the key application that is involved in most of the image based operations. Image segmentation refers to the process of breaking or partitioning any image. Although, like several image processing operations, image segmentation also faces some problems and issues when segmenting process becomes much more complicated. Previously lot of work has proved that Rough-set theory can be a useful method to overcome such complications during image segmentation. The Rough-set theory helps in very fast convergence and in avoiding local minima problem, thereby enhancing the performance of the EM, better result can be achieved. During rough-set-theoretic rule generation, each band is individualized by using the fuzzy-correlation-based gray-level thresholding. Therefore, use of Rough-set in image segmentation can be very useful. In this paper, a summary of all previous Rough-set based image segmentation methods are described in detail and also categorized accordingly. Rough-set based image segmentation provides a stable and better framework for image segmentation.

Continuous Monitoring of Lactate Using Intravascular Microdialysis in High-Risk Cardiac Surgery: A Prospective Observational Study

2017 ◽  
Vol 31 (1) ◽  
pp. 37-44 ◽  
Author(s):  
Pavel I. Lenkin ◽  
Alexey A. Smetkin ◽  
Ayyaz Hussain ◽  
Andrey I. Lenkin ◽  
Konstantin V. Paromov ◽  
...  
Keyword(s):  
Cardiac Surgery ◽  
High Risk ◽  
Observational Study ◽  
Continuous Monitoring ◽  
Prospective Observational Study
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