ADAPTIVE SEGMENTATION OF MEDICAL MR IMAGES BASED ON BIAS CORRECTION

2011 ◽  
Vol 11 (04) ◽  
pp. 813-826
Author(s):  
ZENGLIANG FU ◽  
YONGLIN SU ◽  
MING YE ◽  
YANPING LIN ◽  
CHENGTAO WANG
Keyword(s):  
Bias Correction ◽  
Bias Field ◽  
Correction Method ◽  
Initial Contour ◽  
Adaptive Segmentation ◽  
Mr Images ◽  
Object Boundary ◽  
Correct Position ◽  
Two Phase ◽  
Segmentation Accuracy

A two-phase model is introduced to extract clinically useful information from medical MR images. In the preprocessing phase, a refined bias correction method is adopted to reduce the influence of intensity inhomogeneity by removing the bias field, which paves the way for improving the subsequent segmentation accuracy. During image segmentation process, a novel adaptive level set technique is designed to capture the boundary of desired region. By virtue of adaptive driving term, the external force automatically changes its propagating direction when evolving curve goes through object boundary, which effectively prevents the final results deviating from correct position. Moreover, insensitivity to initial contour also enables its automatic applications. Experiments on synthetic and real MR images demonstrate the feasibility and robustness of the proposed method.

scholarly journals Semantic Segmentation of Underwater Images Based on Improved Deeplab

2020 ◽  
Vol 8 (3) ◽  
pp. 188
Author(s):  
Fangfang Liu ◽  
Ming Fang
Keyword(s):  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Correction Method ◽  
Target Object ◽  
Original Method ◽  
Indoor Navigation ◽  
Fine Tuning ◽  
Object Boundary ◽  
Current State ◽  
Segmentation Accuracy

Image semantic segmentation technology has been increasingly applied in many fields, for example, autonomous driving, indoor navigation, virtual reality and augmented reality. However, underwater scenes, where there is a huge amount of marine biological resources and irreplaceable biological gene banks that need to be researched and exploited, are limited. In this paper, image semantic segmentation technology is exploited to study underwater scenes. We extend the current state-of-the-art semantic segmentation network DeepLabv3 + and employ it as the basic framework. First, the unsupervised color correction method (UCM) module is introduced to the encoder structure of the framework to improve the quality of the image. Moreover, two up-sampling layers are added to the decoder structure to retain more target features and object boundary information. The model is trained by fine-tuning and optimizing relevant parameters. Experimental results indicate that the image obtained by our method demonstrates better performance in improving the appearance of the segmented target object and avoiding its pixels from mingling with other class’s pixels, enhancing the segmentation accuracy of the target boundaries and retaining more feature information. Compared with the original method, our method improves the segmentation accuracy by 3%.

scholarly journals Assimilation of Argo Temperature and Salinity Profiles Using a Bias-Aware EnOI Scheme for the Labrador Sea

2018 ◽  
Vol 35 (9) ◽  
pp. 1819-1834 ◽  
Author(s):  
K. Andrea Scott ◽  
Changheng Chen ◽  
Paul G. Myers
Keyword(s):  
Bias Correction ◽  
North Atlantic Ocean ◽  
Mixed Layer Depth ◽  
Bias Field ◽  
Correction Method ◽  
Ocean Model ◽  
Optimal Interpolation ◽  
Labrador Sea ◽  
The North ◽  
Argo Profiles

AbstractIn this study, temperature and salinity profiles from Argo floats are assimilated into a coupled ice–ocean model over the North Atlantic Ocean and Arctic using an ensemble optimal interpolation (EnOI) scheme, with the aim of improving the thermohaline structure of the Labrador Sea estimated by the model. Data assimilation experiments are carried out from September 2014 to April 2015 both with and without a one-step bias correction method from the literature. It is found that assimilation of the Argo profiles reduces the errors in the model temperature and salinity when verification is done against both withheld Argo profiles and sea surface temperature from satellite data. The assimilation also leads to deeper mixed layer depth in the Labrador Sea, closer to observations shown in other studies, in particular when bias correction is used. We hypothesize that this is because the bias field leads to vertical density profiles that are less stratified, and hence requiring less energy for mixing, than when bias correction is not used.

scholarly journals Deep learning for bias correction of MJO prediction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
H. Kim ◽  
Y. G. Ham ◽  
Y. S. Joo ◽  
S. W. Son
Keyword(s):  
Deep Learning ◽  
Bias Correction ◽  
Numerical Models ◽  
Weather Prediction ◽  
Primary Source ◽  
Correction Method ◽  
Prediction Skill ◽  
Convective System ◽  
Forecast Errors ◽  
Potential Predictability

AbstractProducing accurate weather prediction beyond two weeks is an urgent challenge due to its ever-increasing socioeconomic value. The Madden-Julian Oscillation (MJO), a planetary-scale tropical convective system, serves as a primary source of global subseasonal (i.e., targeting three to four weeks) predictability. During the past decades, operational forecasting systems have improved substantially, while the MJO prediction skill has not yet reached its potential predictability, partly due to the systematic errors caused by imperfect numerical models. Here, to improve the MJO prediction skill, we blend the state-of-the-art dynamical forecasts and observations with a Deep Learning bias correction method. With Deep Learning bias correction, multi-model forecast errors in MJO amplitude and phase averaged over four weeks are significantly reduced by about 90% and 77%, respectively. Most models show the greatest improvement for MJO events starting from the Indian Ocean and crossing the Maritime Continent.

scholarly journals Overestimation of grey matter atrophy in glioblastoma patients following radio(chemo)therapy

2021 ◽  
Author(s):  
A. Gommlich ◽  
F. Raschke ◽  
J. Petr ◽  
A. Seidlitz ◽  
C. Jentsch ◽  
...  
Keyword(s):  
Brain Tumour ◽  
Contralateral Hemisphere ◽  
Mr Images ◽  
Segmentation Accuracy ◽  
Volume Measurements ◽  
Radiation Induced ◽  
The Mean ◽  
Changes Over Time ◽  
Grey Matter Atrophy ◽  
Abnormal Tissue

Abstract Objective Brain atrophy has the potential to become a biomarker for severity of radiation-induced side-effects. Particularly brain tumour patients can show great MRI signal changes over time caused by e.g. oedema, tumour progress or necrosis. The goal of this study was to investigate if such changes affect the segmentation accuracy of normal appearing brain and thus influence longitudinal volumetric measurements. Materials and methods T1-weighted MR images of 52 glioblastoma patients with unilateral tumours acquired before and three months after the end of radio(chemo)therapy were analysed. GM and WM volumes in the contralateral hemisphere were compared between segmenting the whole brain (full) and the contralateral hemisphere only (cl) with SPM and FSL. Relative GM and WM volumes were compared using paired t tests and correlated with the corresponding mean dose in GM and WM, respectively. Results Mean GM atrophy was significantly higher for full segmentation compared to cl segmentation when using SPM (mean ± std: ΔVGM,full = − 3.1% ± 3.7%, ΔVGM,cl = − 1.6% ± 2.7%; p < 0.001, d = 0.62). GM atrophy was significantly correlated with the mean GM dose with the SPM cl segmentation (r = − 0.4, p = 0.004), FSL full segmentation (r = − 0.4, p = 0.004) and FSL cl segmentation (r = -0.35, p = 0.012) but not with the SPM full segmentation (r = − 0.23, p = 0.1). Conclusions For accurate normal tissue volume measurements in brain tumour patients using SPM, abnormal tissue needs to be masked prior to segmentation, however, this is not necessary when using FSL.

An Adaptive Response Surface Method Using Bayesian Metric and Model Bias Correction Function

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
Lei Shi ◽  
Ren-Jye Yang ◽  
Ping Zhu
Keyword(s):  
Response Surface ◽  
Bias Correction ◽  
Adaptive Response ◽  
Large Scale ◽  
Correction Method ◽  
Data Uncertainty ◽  
Correction Function ◽  
Surface Model ◽  
Model Bias ◽  
Adaptive Response Surface Method

The Bayesian metric was used to select the best available response surface in the literature. One of the major drawbacks of this method is the lack of a rigorous method to quantify data uncertainty, which is required as an input. In addition, the accuracy of any response surface is inherently unpredictable. This paper employs the Gaussian process based model bias correction method to quantify the data uncertainty and subsequently improve the accuracy of a response surface model. An adaptive response surface updating algorithm is then proposed for a large-scale problem to select the best response surface. The proposed methodology is demonstrated by a mathematical example and then applied to a vehicle design problem.

STATISTICAL CHARACTERISTICS OF SLANT ANGLES IN HANDWRITTEN NUMERAL STRINGS AND EFFECTS OF SLANT CORRECTION ON SEGMENTATION

2010 ◽  
Vol 24 (01) ◽  
pp. 97-116 ◽  
Author(s):  
JAVAD SADRI ◽  
CHING Y. SUEN ◽  
TIEN D. BUI
Keyword(s):  
Efficient Method ◽  
Weighted Average ◽  
Correction Method ◽  
Statistical Characteristics ◽  
Geometric Features ◽  
Correction Algorithm ◽  
Connected Component ◽  
Slant Angle ◽  
Segmentation Accuracy ◽  
First Time

A novel and efficient method for correction of slant angles in handwritten numeral strings is proposed. For the first time, the statistical distribution of slant angles in handwritten numerals is investigated and the effects of slant correction on the segmentation of handwritten numeral strings are shown. In our proposed slant correction method, utilizing geometric features, a Component Slant Angle (CSA) is estimated for each connected component independently. A weighted average is then used to compute the String Slant Angle (SSA), which is applied uniformly to correct the slant of all the components in numeral strings. Our experimental results have revealed novel statistics for slant angles of handwritten numeral strings, and also showed that slant correction can significantly improve extraction of segmentation features and segmentation accuracy of touching numerals. Comparison between our slant correction algorithm and similar algorithms in the literature show that our algorithm is more efficient, and on average it has a faster running time.

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