Deep semantic segmentation of natural and medical images: a review
The semantic image segmentation task consists of classifying each pixel of an image into an
instance, where each instance corresponds to a class. This task is a part of the concept of …
instance, where each instance corresponds to a class. This task is a part of the concept of …
State of the art survey on MRI brain tumor segmentation
N Gordillo, E Montseny, P Sobrevilla - Magnetic resonance imaging, 2013 - Elsevier
Brain tumor segmentation consists of separating the different tumor tissues (solid or active
tumor, edema, and necrosis) from normal brain tissues: gray matter (GM), white matter (WM) …
tumor, edema, and necrosis) from normal brain tissues: gray matter (GM), white matter (WM) …
f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks
Obtaining expert labels in clinical imaging is difficult since exhaustive annotation is time-
consuming. Furthermore, not all possibly relevant markers may be known and sufficiently …
consuming. Furthermore, not all possibly relevant markers may be known and sufficiently …
Magnetic resonance fingerprinting
Magnetic resonance is an exceptionally powerful and versatile measurement technique. The
basic structure of a magnetic resonance experiment has remained largely unchanged for …
basic structure of a magnetic resonance experiment has remained largely unchanged for …
Current methods in medical image segmentation
▪ Abstract Image segmentation plays a crucial role in many medical-imaging applications, by
automating or facilitating the delineation of anatomical structures and other regions of …
automating or facilitating the delineation of anatomical structures and other regions of …
Image processing with neural networks—a review
M Egmont-Petersen, D de Ridder, H Handels - Pattern recognition, 2002 - Elsevier
We review more than 200 applications of neural networks in image processing and discuss
the present and possible future role of neural networks, especially feed-forward neural …
the present and possible future role of neural networks, especially feed-forward neural …
A survey on brain tumor detection techniques for MR images
PK Chahal, S Pandey, S Goel - Multimedia Tools and Applications, 2020 - Springer
One of the most crucial tasks in any brain tumor detection system is the isolation of abnormal
tissues from normal brain tissues. Interestingly, domain of brain tumor analysis has …
tissues from normal brain tissues. Interestingly, domain of brain tumor analysis has …
Segmentation of tumor and edema along with healthy tissues of brain using wavelets and neural networks
A Demirhan, M Törü, I Güler - IEEE journal of biomedical and …, 2014 - ieeexplore.ieee.org
Robust brain magnetic resonance (MR) segmentation algorithms are critical to analyze
tissues and diagnose tumor and edema in a quantitative way. In this study, we present a …
tissues and diagnose tumor and edema in a quantitative way. In this study, we present a …
An improved intuitionistic fuzzy c-means clustering algorithm incorporating local information for brain image segmentation
H Verma, RK Agrawal, A Sharan - Applied Soft Computing, 2016 - Elsevier
The segmentation of brain magnetic resonance (MR) images plays an important role in the
computer-aided diagnosis and clinical research. However, due to presence of noise and …
computer-aided diagnosis and clinical research. However, due to presence of noise and …
A hybrid feature selection with ensemble classification for imbalanced healthcare data: A case study for brain tumor diagnosis
Electronic health records (EHRs) are providing increased access to healthcare data that can
be made available for advanced data analysis. This can be used by the healthcare …
be made available for advanced data analysis. This can be used by the healthcare …