Results 11 to 20 of about 16,791,752 (293)

Medical imaging analysis with artificial neural networks [PDF]

, 2010
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection ...
Jiang, J., Ren, Jinchang, Trundle, P.
core   +4 more sources

The semiotics of medical image Segmentation [PDF]

Medical Image Analysis, 2018
As the interaction between clinicians and computational processes increases in complexity, more nuanced mechanisms are required to describe how their communication is mediated. Medical image segmentation in particular affords a large number of distinct loci for interaction which can act on a deep, knowledge-driven level which complicates the naive ...
John S. H. Baxter, Eli Gibson, Roy Eagleson, Terry M. Peters   +3 more
openaire   +4 more sources

Loss odyssey in medical image segmentation

Medical Image Analysis, 2021
New segmentation results (based on nnUNetV2) of different loss functions in "Loss Odyssey in Medical Image Segmentation".
Jun Ma 0016, Jianan Chen 0001, Matthew Ng, Rui Huang, Yu Li 0031, Chen Li, Xiaoping Yang 0001, Anne L. Martel   +7 more
openaire   +4 more sources

Medical Image Segmentation Algorithm Based on Optimized Convolutional Neural Network-Adaptive Dropout Depth Calculation

Complexity, 2020
Medical image segmentation is a key technology for image guidance. Therefore, the advantages and disadvantages of image segmentation play an important role in image-guided surgery.
Feng-Ping An, Jun-e Liu
doaj   +1 more source

Automatic texture segmentation for content-based image retrieval application [PDF]

, 2006
In this article, a brief review on texture segmentation is presented, before a novel automatic texture segmentation algorithm is developed. The algorithm is based on a modified discrete wavelet frames and the mean shift algorithm.
Fauzi, M.F.A., Fauzi, Mohammad Faizal Ahmad, Lewis, P.H., Lewis, Paul H.   +3 more
core   +2 more sources

Trends and Techniques in Medical Image Segmentation for Disease Detection [PDF]

ITM Web of Conferences
Medical images have become an indispensable and important tool for the diagnosis of medical conditions and surgical guidance. As computer vision technology advances, Medical image segmentation technology has effectively assisted clinicians in making ...
Jiang Xinli
doaj   +1 more source

Active contours based on weighted gradient vector flow and balloon forces for medical image segmentation [PDF]

, 2014
Active contours, or snakes, have been widely used for image segmentation purposes. However, high noise sensitivity and poor performance over weak edges are the most acute issues that hinder the segmentation accuracy of these curves, particularly in ...
Victor Sanchez, Khadidos, Alaa, Sanchez Silva, Victor, Chang-Tsun Li, Alaa Khadidos, Li, Chang-Tsun   +5 more
core   +1 more source

SEMANTIC SEGMENTATION IN MEDICAL IMAGE ANALYSIS WITH CONVOLUTIONAL NEURAL NETWORKS [PDF]

ICTACT Journal on Image and Video Processing, 2023
Medical image analysis plays a pivotal role in modern healthcare, aiding clinicians in accurate diagnosis and treatment planning. However, the complexity and diversity of medical images pose significant challenges for traditional image processing methods.
Shweta Nishit Jain, Priya Pise, Akhilesh Mishra   +2 more
doaj   +1 more source

A medical image segmentation method based on multi-dimensional statistical features

Frontiers in Neuroscience, 2022
Medical image segmentation has important auxiliary significance for clinical diagnosis and treatment. Most of existing medical image segmentation solutions adopt convolutional neural networks (CNNs).
Yang Xu, Xianyu He, Guofeng Xu, Guanqiu Qi, Kun Yu, Li Yin, Pan Yang, Pan Yang, Yuehui Yin, Hao Chen   +9 more
doaj   +1 more source

Fully Convolutional Network for the Semantic Segmentation of Medical Images: A Survey

Diagnostics, 2022
There have been major developments in deep learning in computer vision since the 2010s. Deep learning has contributed to a wealth of data in medical image processing, and semantic segmentation is a salient technique in this field.
Sheng-Yao Huang, Wen-Lin Hsu, Ren-Jun Hsu, Dai-Wei Liu   +3 more
doaj   +1 more source