scholarly journals COVID-19 Detection using Image Modality: A Review

2021 ◽  
Author(s):  
Prabhleen Kukreja ◽  
Deepak Gupta
Keyword(s):  
Image Modality

scholarly journals Deformable Models for Segmentation Based on Local Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Jimena Olveres ◽  
Erik Carbajal-Degante ◽  
Boris Escalante-Ramírez ◽  
Enrique Vallejo ◽  
Carla María García-Moreno
Keyword(s):  
Local Binary Patterns ◽  
Distance Measures ◽  
Local Analysis ◽  
Hermite Transform ◽  
Active Shape Models ◽  
Shape Models ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Image Modality ◽  
New Algorithms

Segmentation tasks in medical imaging represent an exhaustive challenge for scientists since the image acquisition nature yields issues that hamper the correct reconstruction and visualization processes. Depending on the specific image modality, we have to consider limitations such as the presence of noise, vanished edges, or high intensity differences, known, in most cases, as inhomogeneities. New algorithms in segmentation are required to provide a better performance. This paper presents a new unified approach to improve traditional segmentation methods as Active Shape Models and Chan-Vese model based on level set. The approach introduces a combination of local analysis implementations with classic segmentation algorithms that incorporates local texture information given by the Hermite transform and Local Binary Patterns. The mixture of both region-based methods and local descriptors highlights relevant regions by considering extra information which is helpful to delimit structures. We performed segmentation experiments on 2D images including midbrain in Magnetic Resonance Imaging and heart’s left ventricle endocardium in Computed Tomography. Quantitative evaluation was obtained with Dice coefficient and Hausdorff distance measures. Results display a substantial advantage over the original methods when we include our characterization schemes. We propose further research validation on different organ structures with promising results.

scholarly journals AN AUTOMATED FRAMEWORK FOR CORONARY ANALYSIS FROM CORONARY CINE ANGIOGRAMS USING MACHINE LEARNING AND IMAGE ANALYSIS TECHNIQUES

2021 ◽  
Vol 9 (1) ◽  
pp. 1406-1412
Author(s):  
K. Santhi, A. Rama Mohan Reddy
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Image Analysis ◽  
Visual Assessment ◽  
Disease Diagnosis ◽  
Healthy Life ◽  
Common Cause ◽  
Analysis Techniques ◽  
Image Modality ◽  
Image Analysis Techniques

Cardiovascular disease (CVD) is one of the critical diseases and the most common cause of morbidity and mortality worldwide. Therefore, early detection and prediction of such a disease is extremely essential for a healthy life. Cardiac imaging plays an important role in the diagnosis of cardiovascular disease but its role has been limited to visual assessment of heart structure and its function. However, with the advanced techniques and tools of big data and machine learning, it become easier to clinician to diagnose the CVD. Stenosis with in the Coronary Arteries (CA) are often determined by using the Coronary Cine Angiogram (CCA). It comes under the invasive image modality. CCA is the effective method to detect and predict the stenosis. In this paper a coronary analysis automation method is proposed in disease diagnosis. The proposed method includes pre-processing, segmentation, identifying vessel path and statistical analysis.

scholarly journals Developing intelligent medical image modality classification system using deep transfer learning and LDA

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mehdi Hassan ◽  
Safdar Ali ◽  
Hani Alquhayz ◽  
Khushbakht Safdar
Keyword(s):  
Transfer Learning ◽  
Classification System ◽  
Medical Image ◽  
Image Modality

A COMPARATIVE EVALUATION OF FEATURES FOR MEDICAL IMAGE MODALITY CLASSIFICATION

2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Sameer Ahmad Khan ◽  
Suet Peng Yong ◽  
Uzair Iqbal Janjua
Keyword(s):  
Comparative Evaluation ◽  
Medical Image ◽  
Medical Images ◽  
Image Categorization ◽  
Discriminatory Ability ◽  
Automatic Categorization ◽  
Image Characteristic ◽  
The Stability ◽  
Content Information ◽  
Image Modality

Medical images are increasing at an alarming rate. This increasing number of images affects the interpreting capacity of radiologists. In order to reduce the burden of radiologists, automatic categorization of medical images based on modality is the need of the hour. Because image modality is an important and fundamental image characteristic. The important factor in the automatic medical image categorization based on modality are the features used for categorization purpose, because nice treatment on these subtleties can lead to good results. Many descriptors have been proposed in the literature for medical image categorization. It is unclear which descriptor encodes the content information efficiently. The descriptors that are calculated from these medical images should be descriptive, distinctive and robust to various transformations. The stability of these descriptors are evaluated under various transformations and are then analyzed for their discriminatory ability for the task of classification. In this study the criteria of transformations, repeatability, matching and classification accuracy on the basis of precision recall is used to evaluate the performance of these descriptors. The experimental results illustrates that among global descriptors local features patches histogram and among local descriptors SIFT encodes the content information quite efficiently.

Nontraumatic Proximal Ulnar Artery Pseudoaneurysm Mimicking a Soft Tissue Tumor in Image Modality

2018 ◽  
Vol 47 ◽  
pp. 279.e1-279.e5 ◽  
Author(s):  
Hiromu Kehara ◽  
Yuko Wada ◽  
Daisuke Fukui ◽  
Kunihiko Shingu ◽  
Tatsuichiro Seto ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Soft Tissue Tumor ◽  
Ulnar Artery ◽  
Artery Pseudoaneurysm ◽  
Tissue Tumor ◽  
Image Modality

Text- and content-based biomedical image modality classification

2013 ◽  
Author(s):  
Daekeun You ◽  
Md Mahmudur Rahman ◽  
Sameer Antani ◽  
Dina Demner-Fushman ◽  
George R. Thoma
Keyword(s):  
Biomedical Image ◽  
Image Modality

Which is the preferred image modality for paediatricians when assessing photographs of bruises in children?

2010 ◽  
Vol 125 (6) ◽  
pp. 825-830 ◽  
Author(s):  
Zoë Lawson ◽  
Diane Nuttall ◽  
Stephen Young ◽  
Sam Evans ◽  
Sabine Maguire ◽  
...  
Keyword(s):  
Image Modality

scholarly journals A tale of two images: the quest to create a story-based image indexing system

2014 ◽  
Vol 70 (4) ◽  
pp. 605-621 ◽  
Author(s):  
Pauline Rafferty ◽  
Fawaz Albinfalah
Keyword(s):  
Image Indexing ◽  
Research Subjects ◽  
Structured Interviews ◽  
Content Type ◽  
Input Method ◽  
Indexing System ◽  
Conceptual Paper ◽  
Literary Devices ◽  
Image Modality ◽  
Story Elements

Purpose – The purpose of this conceptual paper is to consider the possibility of designing a story-based image indexing system based on users’ descriptions of images. It reports a pilot study which uses users’ descriptions of two images. Design/methodology/approach – Eight interviews were undertaken to investigate storytelling in user interpretations of the images. Following this, storytelling was explored as an indexing input method. In all, 26 research subjects were asked to create stories about the images, which were then considered in relation to conventional story elements and in relation to Hidderley and Rafferty's (2005) image modality model. Findings – The results of the semi-structured interviews revealed that the majority of interpretations incorporated story elements related to setting, character, plot, literary devices, and themes. The 52 image stories included story elements identified in the first part of the project, and suggested that the image modality model is robust enough to deal with the “writerly” images used in this study. In addition, using storytelling as an input method encourages the use of verbs and connotative level responses. Originality/value – User indexing is generally based on paradigmatic approaches to concept analysis and interpretation in the form of tagging; the novelty of the current study is its exploration of syntagmatic approaches to user indexing in the form of storytelling. It is a pilot, proof of concept study, but it is hoped that it might stimulate further interest in syntagmatic approaches to user indexing.

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