Survey of Image Processing Techniques in Medical Image Analysis: Challenges and Methodologies

2017 ◽  
pp. 460-471 ◽  
Author(s):  
P. Chinmayi ◽  
L. Agilandeeswari ◽  
M. Prabukumar
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Image Processing Techniques ◽  
Processing Techniques

Image processing techniques used for dental x-ray image analysis

2008 ◽  
Author(s):  
Stefan Oprea ◽  
Costin Marinescu ◽  
Ioan Lita ◽  
Mariana Jurianu ◽  
Daniel Alexandru Visan ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
X Ray ◽  
Image Processing Techniques ◽  
Processing Techniques

scholarly journals IDENTIFICATION OF MEGALOBLASTIC ANEMIA CELLS THROUGH THE USE OF IMAGE PROCESSING TECHNIQUES

2018 ◽  
Vol 4 (3) ◽  
pp. 1-5 ◽  
Author(s):  
Asaad Babker ◽  
Vyacheslav Lyashenko
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Blood Smear ◽  
Color Image ◽  
Megaloblastic Anemia ◽  
Cell Structure ◽  
Color Space ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Blood Smear Image

Objective: Our aim is to show the possibility of using different image processing techniques for blood smear analysis. Also our aim is to determine the sequence of image processing techniques to identify megaloblastic anemia cells. Methods: We consider blood smear image. We use a variety of image processing techniques to identify megaloblastic anemia cells. Among these methods, we distinguish the modification of the color space and the use of wavelets. Results: We developed a sequence of image processing techniques for blood smear image analysis and megaloblastic anemia cells identification. As a characteristic feature for megaloblastic anemia cells identification, we consider neutrophil image structure. We also use the morphological methods of image analysis in order to reveal the nuclear lobes in neutrophil structure. Conclusion: We can identify the megaloblastic anemia cells. To do this, we use the following sequence of blood smear image processing: color image modification, change of the image contrast, use of wavelets and morphological analysis of the cell structure. 

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 Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological review

2021 ◽  
Vol 46 (1) ◽  
Author(s):  
R Rashmi ◽  
Keerthana Prasad ◽  
Chethana Babu K Udupa
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Medical Image ◽  
Human Life ◽  
Medical Image Processing ◽  
Histopathological Analysis ◽  
Image Processing Techniques ◽  
Computer Aided ◽  
Histopathological Images ◽  
Processing Techniques

AbstractBreast cancer in women is the second most common cancer worldwide. Early detection of breast cancer can reduce the risk of human life. Non-invasive techniques such as mammograms and ultrasound imaging are popularly used to detect the tumour. However, histopathological analysis is necessary to determine the malignancy of the tumour as it analyses the image at the cellular level. Manual analysis of these slides is time consuming, tedious, subjective and are susceptible to human errors. Also, at times the interpretation of these images are inconsistent between laboratories. Hence, a Computer-Aided Diagnostic system that can act as a decision support system is need of the hour. Moreover, recent developments in computational power and memory capacity led to the application of computer tools and medical image processing techniques to process and analyze breast cancer histopathological images. This review paper summarizes various traditional and deep learning based methods developed to analyze breast cancer histopathological images. Initially, the characteristics of breast cancer histopathological images are discussed. A detailed discussion on the various potential regions of interest is presented which is crucial for the development of Computer-Aided Diagnostic systems. We summarize the recent trends and choices made during the selection of medical image processing techniques. Finally, a detailed discussion on the various challenges involved in the analysis of BCHI is presented along with the future scope.

Silver Nanoparticle (AgNps) Image Analysis using Digital Image Processing Techniques

2021 ◽  
Author(s):  
Parashuram Bannigidad ◽  
Namita Potraj ◽  
Prabhuodeyara M. Gurubasavaraj ◽  
Lakkappa B. Anigol
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Digital Image Processing ◽  
Silver Nanoparticle ◽  
Digital Image ◽  
Image Processing Techniques ◽  
Processing Techniques
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