scholarly journals Medical Image Fusion: A Brief Introduction

2018 ◽  
Vol 11 (3) ◽  
pp. 1209-1214
Author(s):  
Ayush Dogra ◽  
Bhawna Goyal ◽  
Sunil Agrawal
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Image Fusion ◽  
Medical Image ◽  
Huge Amount ◽  
Remote Sensors ◽  
The World ◽  
Basic Understanding ◽  
Insight Into

Digital images are an extremely powerful and widely used medium of communication. They are able to represent very intricate details about the world that surrounds us in a very easy, compact and readily available manner. Due to the innate advances in the acquisition devices such as bio-sensors and remote sensors, huge amount of data is accessible for the further processing and information extraction. The need to efficiently process this immense amount of information has given rise to the emergence of the popular disciplines like image processing, image analysis, and computer vision and image fusion. This article gives a brief insight into basic understanding and significance of image fusion.

Automatic Splicing Technology in Image Splicing Processing

2014 ◽  
Vol 1014 ◽  
pp. 367-370
Author(s):  
Xiao Bo Yu ◽  
Yun Feng Zhang ◽  
Yue Gang Fu
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Computer Graphics ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Research Field ◽  
Important Research ◽  
Image Splicing ◽  
Manual Intervention

Automatic splicing technology is all important research field of image processing, and has become a research focusing on the computer vision and computer graphics,and has important practical value in the fields of image splicing processing, medical image analysis and so on.On the basis of a linear transition method, this paper presents an algorithm which realizes to diminish the seams in overlap region according to the content of scenes.This algorithm avoids manual intervention during the mosaic process.With the help of automatic splicing technology based on the overlapping areas linear transition, the requirement of seamless image splicing can be met. 1.Introduction

scholarly journals Skew correction for mushaf Al-Quran: a review

2020 ◽  
Vol 17 (1) ◽  
pp. 516
Author(s):  
Salem Saleh Bafjaish ◽  
Mohd Sanusi Azmi ◽  
Mohammed Nasser Al-Mhiqani ◽  
Ahmed Abdalla Sheikh
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Arabic Language ◽  
Major Difficulty ◽  
Skew Correction ◽  
The World

Skew correction have been studied a lot recently. However, the content of skew correction in these studies is considered less for Arabic scripts compared to other languages. Different scripts of Arabic language are used by people. Mushaf A-Quran is the book of Allah swt and used by many people around the world. Therefore, skew correction of the pages in Mushaf Al-Quran need to be studied carefully. However, during the process of scanning the pages of Mushaf Al-Quran and due to some other factors, skewed images are produced which will affect the holiness of the Mushaf Al-Quran. However, a major difficulty is the process of detecting the skew and correcting it within the page. Therefore, this paper aims to view the most used skew correction techniques for different scripts as cited in the literature. The findings can be used as a basis for researchers who are interested in image processing, image analysis, and computer vision.

scholarly journals Face Mask Detection System

2021 ◽  
Vol 9 (11) ◽  
pp. 639-642
Author(s):  
Prof. A. T. Sonwane
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Detection System ◽  
Face Mask ◽  
Public Places ◽  
Protection Method ◽  
The World ◽  
Public Areas

Abstract: There are many solutions to prevent the spread of the COVID-19 virus and one of the most effective solutions is wearing a face mask. Almost everyone is wearing face masks at all times in public places during the coronavirus pandemic. Coronavirus disease 2019 has affected the world seriously. One major protection method for people is to wear masks in public areas. The risk of transmission is highest in public places. However, there are only a few research studies about face mask detection based on image analysis. This paper aims to present a review of various methods and algorithms used for human recognition with a face mask. The proposed system to classify face mask detection using COVID-19 precaution both in images and videos using convolution neural network, TensorFlow and OpenCV to detect face masks on people. This system has various applications at public places, schools, etc. where people need to be detected with the presence of a face mask and recognize them and help society. Keywords: COVID-19, Tensorflow, OpenCV, Face Mask, Image Processing, Computer Vision

scholarly journals The Constantly Evolving Role of Medical Image Processing in Oncology: From Traditional Medical Image Processing to Imaging Biomarkers and Radiomics

2021 ◽  
Vol 7 (8) ◽  
pp. 124
Author(s):  
Kostas Marias
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Medical Image ◽  
Biomarker Discovery ◽  
Medical Image Processing ◽  
Machine Learning Techniques ◽  
Imaging Biomarker ◽  
Imaging Biomarkers ◽  
Precision Oncology

The role of medical image computing in oncology is growing stronger, not least due to the unprecedented advancement of computational AI techniques, providing a technological bridge between radiology and oncology, which could significantly accelerate the advancement of precision medicine throughout the cancer care continuum. Medical image processing has been an active field of research for more than three decades, focusing initially on traditional image analysis tasks such as registration segmentation, fusion, and contrast optimization. However, with the advancement of model-based medical image processing, the field of imaging biomarker discovery has focused on transforming functional imaging data into meaningful biomarkers that are able to provide insight into a tumor’s pathophysiology. More recently, the advancement of high-performance computing, in conjunction with the availability of large medical imaging datasets, has enabled the deployment of sophisticated machine learning techniques in the context of radiomics and deep learning modeling. This paper reviews and discusses the evolving role of image analysis and processing through the lens of the abovementioned developments, which hold promise for accelerating precision oncology, in the sense of improved diagnosis, prognosis, and treatment planning of cancer.

scholarly journals PlantCV v2: Image analysis software for high-throughput plant phenotyping

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4088 ◽  
Author(s):  
Malia A. Gehan ◽  
Noah Fahlgren ◽  
Arash Abbasi ◽  
Jeffrey C. Berry ◽  
Steven T. Callen ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Image Data ◽  
Plant Phenotyping ◽  
Analysis Software ◽  
Computational Tools ◽  
Landmark Identification ◽  
Plant Image ◽  
Leaf Segmentation

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

scholarly journals Lungs Cancer Detection Using Digital Image Processing Techniques: A Review

2019 ◽  
Vol 38 (2) ◽  
pp. 351-360 ◽  
Author(s):  
Mati ullah ◽  
Mehwish Bari ◽  
Adeel Ahmed ◽  
Sajid Naveed
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Computer Vision ◽  
Cancer Detection ◽  
Medical Image ◽  
Vital Role ◽  
Classification Methods ◽  
The People ◽  
Medical Diagnostic ◽  
Processing Techniques

From last decade, lung cancer become sign of fear among the people all over the world. As a result, many countries generate funds and give invitation to many scholars to overcome on this disease. Many researchers proposed many solutions and challenges of different phases of computer aided system to detect the lung cancer in early stages and give the facts about the lung cancer. CV (Computer Vision) play vital role to prevent lung cancer. Since image processing is necessary for computer vision, further in medical image processing there are many technical steps which are necessary to improve the performance of medical diagnostic machines. Without such steps programmer is unable to achieve accuracy given by another author using specific algorithm or technique. In this paper we highlight such steps which are used by many author in pre-processing, segmentation and classification methods of lung cancer area detection. If pre-processing and segmentation process have some ambiguity than ultimately it effects on classification process. We discuss such factors briefly so that new researchers can easily understand the situation to work further in which direction.

scholarly journals What is new in computer vision and artificial intelligence in medical image analysis applications

2021 ◽  
Vol 11 (8) ◽  
pp. 3830-3853
Author(s):  
Jimena Olveres ◽  
Germán González ◽  
Fabian Torres ◽  
José Carlos Moreno-Tagle ◽  
Erik Carbajal-Degante ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis

scholarly journals A Survey of Crowdsourcing in Medical Image Analysis

2020 ◽  
Vol 7 ◽  
pp. 1-26 ◽  
Author(s):  
Silas Nyboe Ørting ◽  
Andrew Doyle ◽  
Arno Van Hilten ◽  
Matthias Hirth ◽  
Oana Inel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Image Analysis ◽  
Medical Image ◽  
Large Scale ◽  
Medical Image Analysis ◽  
Machine Learning Algorithms ◽  
Imaging Analysis ◽  
High Quality ◽  
Comprehensive Literature Review

Rapid advances in image processing capabilities have been seen across many domains, fostered by the  application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed, in part, due to the limited availability of large-scale, well-annotated datasets. One of the main reasons for this is the high cost often associated with producing large amounts of high-quality meta-data. Recently, there has been growing interest in the application of crowdsourcing for this purpose; a technique that has proven effective for creating large-scale datasets across a range of disciplines, from computer vision to astrophysics. Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis. In this survey, we review studies applying crowdsourcing to the analysis of medical images, published prior to July 2018. We identify common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach. Finally, we discuss future opportunities for development within this emerging domain.

scholarly journals Review And Analysis Of Computer Vision Algorithms

2021 ◽  
Vol 03 (05) ◽  
pp. 245-250
Author(s):  
Bakhtiyar Saidovich Rakhimov ◽  
◽  
Feroza Bakhtiyarovna Rakhimova ◽  
Sabokhat Kabulovna Sobirova ◽  
Furkat Odilbekovich Kuryazov ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Noise Suppression ◽  
Color Correction ◽  
Scientific Discipline ◽  
Specific Information ◽  
Two Dimensional ◽  
Image Transformation ◽  
Almost All

Computer vision as a scientific discipline refers to the theories and technologies for creating artificial systems that receive information from an image. Despite the fact that this discipline is quite young, its results have penetrated almost all areas of life. Computer vision is closely related to other practical fields like image processing, the input of which is two-dimensional images obtained from a camera or artificially created. This form of image transformation is aimed at noise suppression, filtering, color correction and image analysis, which allows you to directly obtain specific information from the processed image. This information may include searching for objects, keypoints, segments, and annexes;

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