Book ChapterDOI
Intelligent Computing in Medical Imaging: A Study
Shouvik Chakraborty,Sankhadeep Chatterjee,Amira S. Ashour,Kalyani Mali,Nilanjan Dey +4 more
- pp 143-163
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The article was published on 2018-01-01. It has received 50 citations till now. The article focuses on the topics: Medical imaging.read more
Citations
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Journal ArticleDOI
Contrast enhanced medical MRI evaluation using Tsallis entropy and region growing segmentation
N. Sri Madhava Raja,Steven Lawrence Fernandes,Nilanjan Dey,Suresh Chandra Satapathy,Venkatesan Rajinikanth +4 more
TL;DR: This paper proposes a semi-automated tool to investigate the medical MRI captured with contrast improved T1 modality (T1C), which considers the integration of Bat algorithm and Tsallis based thresholding along with region growing (RG) segmentation.
Journal ArticleDOI
SuFMoFPA: A superpixel and meta-heuristic based fuzzy image segmentation approach to explicate COVID-19 radiological images
Shouvik Chakraborty,Kalyani Mali +1 more
TL;DR: A novel method is proposed in this work to segment the Radiological images for the better explication of the COVID-19 radiological images, known as SuFMoFPA (Superpixel based Fuzzy Modified Flower Pollination Algorithm).
Journal ArticleDOI
Fetal Head Periphery Extraction from Ultrasound Image using Jaya Algorithm and Chan-Vese Segmentation
TL;DR: The results of experimental work substantiate that, the hybrid procedure is capable in examining the 2DUI and offers enhanced picture similarity measures during the FHC examination.
Book ChapterDOI
Application of Multiobjective Optimization Techniques in Biomedical Image Segmentation—A Study
Shouvik Chakraborty,Kalyani Mali +1 more
TL;DR: The main goal of this chapter is to give a comprehensive study of multiobjective optimization techniques in biomedical image analysis problem that consolidated some of the recent works along with future directions.
Book ChapterDOI
An Overview of Biomedical Image Analysis From the Deep Learning Perspective
Shouvik Chakraborty,Kalyani Mali +1 more
TL;DR: In this chapter, a comprehensive overview of the deep learning-assisted biomedical image analysis methods is presented and can be helpful for the researchers to understand the recent developments and drawbacks of the present systems.
References
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Journal ArticleDOI
Computer-Aided Diagnosis in Medical Imaging: Historical Review, Current Status and Future Potential
TL;DR: The motivation and philosophy for early development of CAD schemes are presented together with the current status and future potential of CAD in a PACS environment.
Journal ArticleDOI
Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer.
TL;DR: It is concluded that three-layer, feed-forward neural networks with a back-propagation algorithm trained for the interpretation of mammograms on the basis of features extracted from mammograms by experienced radiologists may provide a potentially useful tool in the mammographic decision-making task of distinguishing between benign and malignant lesions.
Journal ArticleDOI
Review of neural network applications in medical imaging and signal processing.
TL;DR: The current applications of neural networks to in vivo medical imaging and signal processing are reviewed and a description of recent studies is provided to provide an appreciation of the problems associated with implementing neural networks for medical imagingand signal processing.
Journal ArticleDOI
Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images
TL;DR: The authors show that very good diagnostic rates can be obtained using unconventional classifiers trained on actual patient data, and set to have minimum classification error, ease of implementation and learning, and the flexibility for future modifications.
Journal ArticleDOI
An automatic diagnostic system for CT liver image classification
TL;DR: A CT liver image diagnostic classification system which will automatically find, extract the CT liver boundary and further classify liver diseases is presented and shown to be efficient and very effective.