Results 31 to 40 of about 169,019 (308)

How interaction methods affect image segmentation: user experience in the task [PDF]

, 2013
Interactive image segmentation is extensively used in photo editing when the aim is to separate a foreground object from its background so that it is available for various applications.
Healy, Graham, McGuinness, Kevin, Hebbalaguppe, Ramya, G. Healy, Alan Smeaton, K. McGuinness, O'Connor, Noel E., Noel O'Connor, Smeaton, Alan F., Kuklyte, Jogile, O\u27Connor, Noel E., R. Hebbalaguppe, J. Kuklyte   +12 more
core   +1 more source

MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning

BMC Medical Imaging, 2021
Background The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. Still, current image segmentation platforms do not provide the required functionalities for plain setup of medical ...
Dominik Müller, Frank Kramer
doaj   +1 more source

Multi-scale Gaussian representation and outline-learning based cell image segmentation [PDF]

, 2013
BACKGROUND: High-throughput genome-wide screening to study gene-specific functions, e.g. for drug discovery, demands fast automated image analysis methods to assist in unraveling the full potential of such studies.
Rämö, P., Mario Emmenlauer, Ruusuvuori, P., Pekka Ruusuvuori, Dehio, C., Yli-Harja, O., Christoph Dehio, Emmenlauer, M., Olli Yli-Harja, Farhan, M., Muhammad Farhan, Pauli Rämö   +11 more
core   +1 more source

Medical Image Segmentation Algorithm for Three-Dimensional Multimodal Using Deep Reinforcement Learning and Big Data Analytics

Frontiers in Public Health, 2022
To avoid the problems of relative overlap and low signal-to-noise ratio (SNR) of segmented three-dimensional (3D) multimodal medical images, which limit the effect of medical image diagnosis, a 3D multimodal medical image segmentation algorithm using ...
Weiwei Gao, Xiaofeng Li, Yanwei Wang, Yingjie Cai   +3 more
doaj   +1 more source

DCSLK: Combined large kernel shared convolutional model with dynamic channel Sampling

NeuroImage
This study centers around the competition between Convolutional Neural Networks (CNNs) with large convolutional kernels and Vision Transformers in the domain of computer vision, delving deeply into the issues pertaining to parameters and computational ...
Zongren Li, Shuping Luo, Hongwei Li, Yanbin Li   +3 more
doaj   +1 more source

A comparative evaluation of interactive segmentation algorithms [PDF]

, 2010
In this paper we present a comparative evaluation of four popular interactive segmentation algorithms. The evaluation was carried out as a series of user-experiments, in which participants were tasked with extracting 100 objects from a common dataset: 25
McGuinness, Kevin, O'Connor, Noel E., O\u27Connor, Noel E.   +2 more
core   +1 more source

Web-based AI system for medical image segmentation [PDF]

, 2023
Image segmentation is a crucial step in the diagnosis of brain tumours, and machine learning has emerged as a promising tool for tumour characterisation from medical imaging data.
Li, Yiqi, Tao, Sihan, Fetit, Ahmed, Liu, Taowen, Lee, Jacqueline, Hu, Songyun, Yu, Leyang, Chen, Hao   +7 more
core   +1 more source

Implementation and evaluation of segmentation algorithms according to multimodal imaging in personalized medicine

Current Directions in Biomedical Engineering, 2017
Multimodal imaging is gaining in importance in the field of personalized medicine and can be described as a current trend in medical imaging diagnostics. The segmentation, classification and analysis of tissue structures is essential.
Stich Manuel, Vogt Jeannine, Lindner Michaela, Ringler Ralf   +3 more
doaj   +1 more source

Application of Image Segmentation Technology Based on Machine Learning in Medical Image Analysis [PDF]

ITM Web of Conferences
Medical image analysis heavily relies on the crucial step of image segmentation, which possesses the capability to discern and differentiate various structures within medical imagery.
Zhang Yurun
doaj   +1 more source

Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition

Sensors, 2023
This paper explored techniques for diagnosing breast cancer using deep learning based medical image recognition. X-ray (Mammography) images, ultrasound images, and histopathology images are used to improve the accuracy of the process by diagnosing breast
Deawon Kwak, Jiwoo Choi, Sungjin Lee
doaj   +1 more source