Results 61 to 70 of about 340,403 (177)

Medical Image Segmentation Using Combined Level Set and Saliency Analysis

IEEE Access
In the realm of computer vision, image segmentation has become a crucial task with widespread applications, particularly in medical imaging. Although there have been significant advancements in image segmentation methods, challenges persist in accurately
Aditi Joshi, Mohammed Saquib Khan, Kwang Nam Choi   +2 more
doaj   +1 more source

A survey of fuzzy rule-based image segmentation techniques [PDF]

, 2000
This paper describes the various fuzzy rule based techniques for image segmentation. Fuzzy rule based segmentation techniques can incorporate domain expert knowledge and manipulate numerical as well as linguistic data.
Dooley, Laurence S., Karmakar, Gour C., Rahman, Syed   +2 more
core  

Segmenting medical images with limited data

Neural Networks
While computer vision has proven valuable for medical image segmentation, its application faces challenges such as limited dataset sizes and the complexity of effectively leveraging unlabeled images. To address these challenges, we present a novel semi-supervised, consistency-based approach termed the data-efficient medical segmenter (DEMS).
Zhaoshan Liu, Qiujie Lv, Chau Hung Lee, Lei Shen   +3 more
openaire   +3 more sources

Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images [PDF]

, 2018
We propose a novel attention gate (AG) model for medical image analysis that automatically learns to focus on target structures of varying shapes and sizes.
Glocker, Ben, Heinrich, Mattias, Kainz, Bernhard, Oktay, Ozan, Rueckert, Daniel, Schaap, Michiel, Schlemper, Jo   +6 more
core   +1 more source

Advantages of transformer and its application for medical image segmentation: a survey

BioMedical Engineering OnLine
Purpose Convolution operator-based neural networks have shown great success in medical image segmentation over the past decade. The U-shaped network with a codec structure is one of the most widely used models.
Qiumei Pu, Zuoxin Xi, Shuai Yin, Zhe Zhao, Lina Zhao   +4 more
doaj   +1 more source

DA-TransUNet: integrating spatial and channel dual attention with transformer U-net for medical image segmentation

Frontiers in Bioengineering and Biotechnology
Accurate medical image segmentation is critical for disease quantification and treatment evaluation. While traditional U-Net architectures and their transformer-integrated variants excel in automated segmentation tasks. Existing models also struggle with
Guanqun Sun, Guanqun Sun, Yizhi Pan, Weikun Kong, Zichang Xu, Jianhua Ma, Teeradaj Racharak, Le-Minh Nguyen, Junyi Xin, Junyi Xin, Junyi Xin   +10 more
doaj   +1 more source

Token Sparsification for Faster Medical Image Segmentation

, 2023
Accepted to IPMI'23.
Zhou, Lei, Liu, Huidong, Bae, Joseph, He, Junjun, Samaras, Dimitris, Prasanna, Prateek   +5 more
openaire   +3 more sources

Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches

Bioengineering
Medical image segmentation plays a critical role in accurate diagnosis and treatment planning, enabling precise analysis across a wide range of clinical tasks. This review begins by offering a comprehensive overview of traditional segmentation techniques,
Yan Xu, Rixiang Quan, Weiting Xu, Yi Huang, Xiaolong Chen, Fengyuan Liu   +5 more
doaj   +1 more source

BRAIN MR IMAGE SEGMENTATION BY MODIFIED ACTIVE CONTOURS AND CONTOURLET TRANSFORM

ICTACT Journal on Image and Video Processing, 2017
Multiresolution analysis is often used for image representation and processing because it can represent image at the split resolution and scale space.
P Nageswara Reddy, C P V N J Mohan Rao, Ch Satyanarayana   +2 more
doaj   +1 more source

Medical Image Segmentation by Water Flow

, 2007
We present a new image segmentation technique based on the paradigm of water flow and apply it to medical images. The force field analogy is used to implement the major water flow attributes like water pressure, surface tension and adhesion so that the ...
Liu, Xin, Nixon, Mark
core