19 research outputs found
Fully Automatic Ultrasound Fetal Heart Image Detection and Segmentation based on Texture Analysis
Ultrasound fetal heart image analysis is important for the antenatal diagnosis of congenital heart disease, therefore, design an automated fetal heart ultrasound image analysis approaches to improve detection ratio of congenital heart disease is necessary. Nevertheless, because of the complicated structure of fetal heart ultrasound image, location, detection and segmentation approaches of fetal heart images as interesting topics that get more attention. Therefore, in this work, we present a framework to segment ultrasound image automatically for tracking the boundary of fetal heart region. In the first step, this paper contributes to breed candidate regions. And then, in the segmentation progress, we apply an energy-based active contour model to detect the edges of fetal heart. Finally, in the experiment section, the performance is estimated by the Dice similarity coefficient, which calculate the spatial overlap between two different segmentation regions, and the experiment results indicate that the proposed algorithm achieves high levels of accuracy
A Modified Brain MR Image Segmentation and Bias Field Estimation Model Based on Local and Global Information
Because of the poor radio frequency coil uniformity and gradient-driven eddy currents, there is much noise and intensity inhomogeneity (bias) in brain magnetic resonance (MR) image, and it severely affects the segmentation accuracy. Better segmentation results are difficult to achieve by traditional methods; therefore, in this paper, a modified brain MR image segmentation and bias field estimation model based on local and global information is proposed. We first construct local constraints including image neighborhood information in Gaussian kernel mapping space, and then the complete regularization is established by introducing nonlocal spatial information of MR image. The weighting between local and global information is automatically adjusted according to image local information. At the same time, bias field information is coupled with the model, and it makes the model reduce noise interference but also can effectively estimate the bias field information. Experimental results demonstrate that the proposed algorithm has strong robustness to noise and bias field is well corrected
Segmenting Medical MRI via Recurrent Decoding Cell
The encoder-decoder networks are commonly used in medical image segmentation
due to their remarkable performance in hierarchical feature fusion. However,
the expanding path for feature decoding and spatial recovery does not consider
the long-term dependency when fusing feature maps from different layers, and
the universal encoder-decoder network does not make full use of the
multi-modality information to improve the network robustness especially for
segmenting medical MRI. In this paper, we propose a novel feature fusion unit
called Recurrent Decoding Cell (RDC) which leverages convolutional RNNs to
memorize the long-term context information from the previous layers in the
decoding phase. An encoder-decoder network, named Convolutional Recurrent
Decoding Network (CRDN), is also proposed based on RDC for segmenting
multi-modality medical MRI. CRDN adopts CNN backbone to encode image features
and decode them hierarchically through a chain of RDCs to obtain the final
high-resolution score map. The evaluation experiments on BrainWeb, MRBrainS and
HVSMR datasets demonstrate that the introduction of RDC effectively improves
the segmentation accuracy as well as reduces the model size, and the proposed
CRDN owns its robustness to image noise and intensity non-uniformity in medical
MRI.Comment: 8 pages, 7 figures, AAAI-2
Unsupervised color texture segmentation based on multi-scale region-level Markov random field models
In the field of color texture segmentation, region-level Markov random field model (RMRF) has become a focal problem because of its efficiency in modeling the large-range spatial constraints. However, the RMRF defined on a single scale cannot describe the un-stationary essence of the image, which highly limits its robustness. Hence, by combining wavelet transformation and the RMRF model, we present a multi-scale RMRF (MsRMRF) model in wavelet domainin this paper. In the Bayesian framework, the proposed model seamlessly integrates the multi-scale information stemmed from both the original image and the region-level spatial constraints. Therefore, the new model can accurately describe the characteristics of different kinds of texture. Based on MsRMRF, an unsupervised segmentation algorithm is designed for segmenting color texture images. Both synthetic color texture images and remote sensing images are employed in the comparative experiments, and the experimental results show that the proposed method can obtain more accurate segmentation results than the competitors.This work was financially supported by the Key Technology Projects of Henan province of China under Grant 15210241004, Supported by Program for Changjiang Scholars and Innovative Research Team in University, the Key Technology Projects of Henan Educational Department of China under Grant 16A520036, the Key Technology Projects of Henan Educational Department of China under Grant 16B520001,the National Natural Science Foundation of China under Grant 41001251, Anyang science and technology plan project: Researches on Road Extraction Algorithm based on MRF for High Resolution Remote Sensing Image, and the Research and Cultivation Fund Project of Anyang Normal University under Grant AYNU-KP-B08
Robust Fuzzy C-Means Clustering Algorithm Based on Normal Shrink and Membership Filtering for Image Segmentation
The robustness and effectiveness of image segmentation using the FCM algorithm can be improved by incorporating local spatial information into the FCM method, which is particularly noise-tolerant. However the introduction of local spatial information gives more computational complexity. Hence to overcome this problem an improved FCM clustering method is proposed which is based on a normal shrink algorithm with membership filtering. The Proposed method gives a faster and more robust result in comparison to FCM. Firstly, a normal shrink denoising algorithm is introduced to preserve the image details and noise immunity. Secondly, membership filtering is introduced, which depends only on the local spatial neighboring properties of the matrix called the membership partition matrix. The Proposed method is faster and simpler as it does not calculate the distance between pixels and cluster centers and between local spatial neighboring. Also, it is very efficient for noise immunity
Residual-Sparse Fuzzy -Means Clustering Incorporating Morphological Reconstruction and Wavelet frames
Instead of directly utilizing an observed image including some outliers,
noise or intensity inhomogeneity, the use of its ideal value (e.g. noise-free
image) has a favorable impact on clustering. Hence, the accurate estimation of
the residual (e.g. unknown noise) between the observed image and its ideal
value is an important task. To do so, we propose an
regularization-based Fuzzy -Means (FCM) algorithm incorporating a
morphological reconstruction operation and a tight wavelet frame transform. To
achieve a sound trade-off between detail preservation and noise suppression,
morphological reconstruction is used to filter an observed image. By combining
the observed and filtered images, a weighted sum image is generated. Since a
tight wavelet frame system has sparse representations of an image, it is
employed to decompose the weighted sum image, thus forming its corresponding
feature set. Taking it as data for clustering, we present an improved FCM
algorithm by imposing an regularization term on the residual between
the feature set and its ideal value, which implies that the favorable
estimation of the residual is obtained and the ideal value participates in
clustering. Spatial information is also introduced into clustering since it is
naturally encountered in image segmentation. Furthermore, it makes the
estimation of the residual more reliable. To further enhance the segmentation
effects of the improved FCM algorithm, we also employ the morphological
reconstruction to smoothen the labels generated by clustering. Finally, based
on the prototypes and smoothed labels, the segmented image is reconstructed by
using a tight wavelet frame reconstruction operation. Experimental results
reported for synthetic, medical, and color images show that the proposed
algorithm is effective and efficient, and outperforms other algorithms.Comment: 12 pages, 11 figur