1,042 research outputs found
A distance regularized level-set evolution model based MRI dataset segmentation of brain’s caudate nucleus
The caudate nucleus of the brain is highly correlated to the emotional decision-making of pessimism, which is an important process for improving the understanding and treatment of depression; and the segmentation of the caudate nucleus is the most basic step in the process of analysis and research concerning this region. In this paper, Level Set Method (LSM) is applied for caudate nucleus segmentation. Firstly, Distance Regularized Level Set Evolution (DRLSE), Region-Scalable Fitting (RSF) and Local Image Fitting (LIF) models are proposed for segmentation of the caudate nucleus of Magnetic Resonance Imaging (MRI) images of the brain, and the segmentation results are compared by using selected evaluation indices. The average Dice Similarity Coefficient (DSC) values of the proposed three methods all exceed 85%, and the average Jaccard Similarity (JS) values are over 77%, respectively. The results indicate that all these three models can have good segmentation results for medical images with intensity inhomogeneity and meet the general segmentation requirements, while the proposed DRLSE model performs better in segmentation
On using an analogy to heat flow for shape extraction
We introduce a novel evolution-based segmentationalgorithm which uses the heat flow analogy togain practical advantage. The proposed algorithm consistsof two parts. In the first part, we represent a particular heatconduction problem in the image domain to roughly segmentthe region of interest. Then we use geometric heatflow to complete the segmentation, by smoothing extractedboundaries and removing noise inside the prior segmentedregion. The proposed algorithm is compared with activecontour models and is tested on synthetic and medicalimages. Experimental results indicate that our approachworks well in noisy conditions without pre-processing. Itcan detect multiple objects simultaneously. It is alsocomputationally more efficient and easier to control andimplement in comparison with active contour models
Re-initialization-free Level Set Method via Molecular Beam Epitaxy Equation Regularization for Image Segmentation
Variational level set method has become a powerful tool in image segmentation
due to its ability to handle complex topological changes and maintain
continuity and smoothness in the process of evolution. However its evolution
process can be unstable, which results in over flatted or over sharpened
contours and segmentation failure. To improve the accuracy and stability of
evolution, we propose a high-order level set variational segmentation method
integrated with molecular beam epitaxy (MBE) equation regularization. This
method uses the crystal growth in the MBE process to limit the evolution of the
level set function, and thus can avoid the re-initialization in the evolution
process and regulate the smoothness of the segmented curve. It also works for
noisy images with intensity inhomogeneity, which is a challenge in image
segmentation. To solve the variational model, we derive the gradient flow and
design scalar auxiliary variable (SAV) scheme coupled with fast Fourier
transform (FFT), which can significantly improve the computational efficiency
compared with the traditional semi-implicit and semi-explicit scheme. Numerical
experiments show that the proposed method can generate smooth segmentation
curves, retain fine segmentation targets and obtain robust segmentation results
of small objects. Compared to existing level set methods, this model is
state-of-the-art in both accuracy and efficiency
Hybrid Active Contour Based on Local and Global Statistics Parameterized by Weight Coefficients for Inhomogeneous Image Segmentation
Image inhomogeneity often occurs in real-world images and may present considerable difficulties during image segmentation. Therefore, this paper presents a new approach for the segmentation of inhomogeneous images. The proposed hybrid active contour model is formulated by combining the statistical information of both the local and global region-based energy fitting models. The inclusion of the local region-based energy fitting model assists in extracting the inhomogeneous intensity regions, whereas the curve evolution over the homogeneous regions is accelerated by including the global region-based model in the proposed method. Both the local and global region-based energy functions in the proposed model drag contours toward the accurate object boundaries with precision. Each of the local and global region-based parts are parameterized with weight coefficients, based on image complexity, to modulate two parts. The proposed hybrid model is strongly capable of detecting region of interests (ROIs) in the presence of complex object boundaries and noise, as its local region-based part comprises bias field. Moreover, the proposed method includes a new bias field (NBF) initialization and eliminates the dependence over the initial contour position. Experimental results on synthetic and real-world images, produced by the proposed model, and comparative analysis with previous state-of-the-art methods confirm its superior performance in terms of both time efficiency and segmentation accuracy
Segmentation of Intensity-Corrupted Medical Images Using Adaptive Weight-Based Hybrid Active Contours
6Segmentation accuracy is an important criterion for evaluating the performance of segmentation techniques used to extract objects of interest from images, such as the active contour model. However, segmentation accuracy can be affected by image artifacts such as intensity inhomogeneity, which makes it difficult to extract objects with inhomogeneous intensities. To address this issue, this paper proposes a hybrid region-based active contour model for the segmentation of inhomogeneous images. The proposed hybrid energy functional combines local and global intensity functions; an incorporated weight function is parameterized based on local image contrast. The inclusion of this weight function smoothens the contours at different intensity level boundaries, thereby yielding improved segmentation. The weight function suppresses false contour evolution and also regularizes object boundaries. Compared with other state-of-the-art methods, the proposed approach achieves superior results over synthetic and real images. Based on a quantitative analysis over the mini-MIAS and PH2 databases, the superiority of the proposed model in terms of segmentation accuracy, as compared with the ground truths, was confirmed. Furthermore, when using the proposed model, the processing time for image segmentation is lower than those when using other methods.openopenMemon A.A.; Soomro S.; Shahid M.T.; Munir A.; Niaz A.; Choi K.N.Memon, A. A.; Soomro, S.; Shahid, M. T.; Munir, A.; Niaz, A.; Choi, K. N
Hybrid SPF and KD Operator-Based Active Contour Model for Image Segmentation
Image segmentation is a crucial stage of image analysis systems because it detects and extracts regions of interest for further processing, such as image recognition and the image description. However, segmenting images is not always easy because segmentation accuracy depends significantly on image characteristics, such as color, texture, and intensity. Image inhomogeneity profoundly degrades the segmentation performance of segmentation models. This article contributes to image segmentation literature by presenting a hybrid Active Contour Model (ACM) based on a Signed Pressure Force (SPF) function parameterized with a Kernel Difference (KD) operator. An SPF function includes information from both the local and global regions, making the proposed model independent of the initial contour position. The proposed model uses an optimal KD operator parameterized with weight coefficients to capture weak and blurred boundaries of inhomogeneous objects in images. Combined global and local image statistics were computed and added to the proposed energy function to increase the proposed model's sensitivity. The segmentation time complexity of the proposed model was calculated and compared with previous state-of-the-art active contour methods. The results demonstrated the significant superiority of the proposed model over other methods. Furthermore, a quantitative analysis was performed using the mini-MIAS database. Despite the presence of complex inhomogeneity, the proposed model demonstrated the highest segmentation accuracy when compared to other methods
Convex Image Segmentation Model Based on Local and Global Intensity Fitting Energy and Split Bregman Method
We propose a convex image segmentation model in a variational level set formulation. Both the local information and the global information are taken into consideration to get better segmentation results. We first propose a globally convex energy functional to combine the local and global intensity fitting terms. The proposed energy functional is then modified by adding an edge detector to force the active contour to the boundary more easily. We then apply the split Bregman method to minimize the proposed energy functional efficiently. By using a weight function that varies with location of the image, the proposed model can balance the weights between the local and global fitting terms dynamically. We have applied the proposed model to synthetic and real images with desirable results. Comparison with
other models also demonstrates the accuracy and superiority of the proposed model
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
- …