1,015 research outputs found
Markov random field segmentation for industrial computed tomography with metal artefacts
X-ray Computed Tomography (XCT) has become an important tool for industrial measurement and quality control through its ability to measure internal structures and volumetric defects. Segmentation of constituent materials in the volume acquired through XCT is one of the most critical factors that influence its robustness and repeatability. Highly attenuating materials such as steel can introduce artefacts in CT images that adversely affect the segmentation process, and results in large errors during quantification. This paper presents a Markov Random Field (MRF) segmentation method as a suitable approach for industrial samples with metal artefacts. The advantages of employing the MRF segmentation method are shown in comparison with Otsu thresholding on CT data from two industrial objects
Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models
Segmentation is a fundamental task for extracting semantically meaningful
regions from an image. The goal of segmentation algorithms is to accurately
assign object labels to each image location. However, image-noise, shortcomings
of algorithms, and image ambiguities cause uncertainty in label assignment.
Estimating the uncertainty in label assignment is important in multiple
application domains, such as segmenting tumors from medical images for
radiation treatment planning. One way to estimate these uncertainties is
through the computation of posteriors of Bayesian models, which is
computationally prohibitive for many practical applications. On the other hand,
most computationally efficient methods fail to estimate label uncertainty. We
therefore propose in this paper the Active Mean Fields (AMF) approach, a
technique based on Bayesian modeling that uses a mean-field approximation to
efficiently compute a segmentation and its corresponding uncertainty. Based on
a variational formulation, the resulting convex model combines any
label-likelihood measure with a prior on the length of the segmentation
boundary. A specific implementation of that model is the Chan-Vese segmentation
model (CV), in which the binary segmentation task is defined by a Gaussian
likelihood and a prior regularizing the length of the segmentation boundary.
Furthermore, the Euler-Lagrange equations derived from the AMF model are
equivalent to those of the popular Rudin-Osher-Fatemi (ROF) model for image
denoising. Solutions to the AMF model can thus be implemented by directly
utilizing highly-efficient ROF solvers on log-likelihood ratio fields. We
qualitatively assess the approach on synthetic data as well as on real natural
and medical images. For a quantitative evaluation, we apply our approach to the
icgbench dataset
Approximate Lesion Localization in Dermoscopy Images
Background: Dermoscopy is one of the major imaging modalities used in the
diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty
and subjectivity of human interpretation, automated analysis of dermoscopy
images has become an important research area. Border detection is often the
first step in this analysis. Methods: In this article, we present an
approximate lesion localization method that serves as a preprocessing step for
detecting borders in dermoscopy images. In this method, first the black frame
around the image is removed using an iterative algorithm. The approximate
location of the lesion is then determined using an ensemble of thresholding
algorithms. Results: The method is tested on a set of 428 dermoscopy images.
The localization error is quantified by a metric that uses dermatologist
determined borders as the ground truth. Conclusion: The results demonstrate
that the method presented here achieves both fast and accurate localization of
lesions in dermoscopy images
Effective Cloud Detection and Segmentation using a Gradient-Based Algorithm for Satellite Imagery; Application to improve PERSIANN-CCS
Being able to effectively identify clouds and monitor their evolution is one
important step toward more accurate quantitative precipitation estimation and
forecast. In this study, a new gradient-based cloud-image segmentation
technique is developed using tools from image processing techniques. This
method integrates morphological image gradient magnitudes to separable cloud
systems and patches boundaries. A varying scale-kernel is implemented to reduce
the sensitivity of image segmentation to noise and capture objects with various
finenesses of the edges in remote-sensing images. The proposed method is
flexible and extendable from single- to multi-spectral imagery. Case studies
were carried out to validate the algorithm by applying the proposed
segmentation algorithm to synthetic radiances for channels of the Geostationary
Operational Environmental Satellites (GOES-R) simulated by a high-resolution
weather prediction model. The proposed method compares favorably with the
existing cloud-patch-based segmentation technique implemented in the
PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using
Artificial Neural Network - Cloud Classification System) rainfall retrieval
algorithm. Evaluation of event-based images indicates that the proposed
algorithm has potential to improve rain detection and estimation skills with an
average of more than 45% gain comparing to the segmentation technique used in
PERSIANN-CCS and identifying cloud regions as objects with accuracy rates up to
98%
Maximum a posteriori spatial probability segmen
An image segmentation algorithm that performs pixel-by-pixel segmentation on an image with consideration of the spatial information is described. The spatial information is the joint grey level values of the pixel to be segmented and its neighbouring pixels. The conditional probability that a pixel belongs to a particular class under the condition that the spatial information has been observed is defined to be the a posteriori spatial probability. A maximum a posteriori spatial probability (MASP) segmentation algorithm is proposed to segment an image such that each pixel is segmented into a particular class when the a posteriori spatial probability is a maximum. The proposed segmentation algorithm is implemented in an iterative form. During the iteration, a series of intermediate segmented images are produced among which the one that possesses the maximum amount of information in its spatial structure is chosen as the optimum segmented image. Results from segmenting synthetic and practical images demonstrate that the MASP algorithm is capable of achieving better results when compared with other global thresholding methods.published_or_final_versio
Texture Image Segmentation using Morphology in Wavelet Transforms
One of the essential and crucial steps for image understanding, interpretation, analysis and recognition is the image segmentation. This paper advocates a new segme- ntation scheme using morphology on wavelet decomposed images. The present paper provides a good segmentation on natural images and textures by dividing an image into non overlapping regions, which are homogenous in terms of certain features such as texture, spatial coordinates etc. using simple morphological operations. Morphological enhancement technique based on Top Hat transforms enhances the local contrast in this paper. The morphological treatment and followed by Otsu2019;s threshold overcomes the problem of noise and thin gaps, and also smooth the final regions. The experimental results on four different databases demonstrate the success of the proposed method, compared to many other methods
Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping
Cosegmentation is a newly emerging computer vision technique used to segment an object from the background by processing multiple images at the same time. Traditional plant phenotyping analysis uses thresholding segmentation methods which result in high segmentation accuracy. Although there are proposed machine learning and deep learning algorithms for plant segmentation, predictions rely on the specific features being present in the training set. The need for a multi-featured dataset and analytics for cosegmentation becomes critical to better understand and predict plants’ responses to the environment. High-throughput phenotyping produces an abundance of data that can be leveraged to improve segmentation accuracy and plant phenotyping. This paper introduces four datasets consisting of two plant species, Buckwheat and Sunflower, each split into control and drought conditions. Each dataset has three modalities (Fluorescence, Infrared, and Visible) with 7 to 14 temporal images that are collected in a high-throughput facility at the University of Nebraska-Lincoln. The four datasets (which will be collected under the CosegPP data repository in this paper) are evaluated using three cosegmentation algorithms: Markov random fields-based, Clustering-based, and Deep learning-based cosegmentation, and one commonly used segmentation approach in plant phenotyping. The integration of CosegPP with advanced cosegmentation methods will be the latest benchmark in comparing segmentation accuracy and finding areas of improvement for cosegmentation methodology
Automatic segmentation and classification methods using optical coherence tomography angiography (Octa): A review and handbook
Optical coherence tomography angiography (OCTA) is a promising technology for the non-invasive imaging of vasculature. Many studies in literature present automated algorithms to quantify OCTA images, but there is a lack of a review on the most common methods and their comparison considering multiple clinical applications (e.g., ophthalmology and dermatology). Here, we aim to provide readers with a useful review and handbook for automatic segmentation and classification methods using OCTA images, presenting a comparison of techniques found in the literature based on the adopted segmentation or classification method and on the clinical application. Another goal of this study is to provide insight into the direction of research in automated OCTA image analysis, especially in the current era of deep learning
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