2,672 research outputs found
Adaptive smoothness constraint image multilevel fuzzy enhancement algorithm
For the problems of poor enhancement effect and long time consuming of the traditional algorithm, an adaptive smoothness constraint image multilevel fuzzy enhancement algorithm based on secondary color-to-grayscale conversion is proposed. By using fuzzy set theory and generalized fuzzy set theory, a new linear generalized fuzzy operator transformation is carried out to obtain a new linear generalized fuzzy operator. By using linear generalized membership transformation and inverse transformation, secondary color-to-grayscale conversion of adaptive smoothness constraint image is performed. Combined with generalized fuzzy operator, the region contrast fuzzy enhancement of adaptive smoothness constraint image is realized, and image multilevel fuzzy enhancement is realized. Experimental results show that the fuzzy degree of the image is reduced by the improved algorithm, and the clarity of the adaptive smoothness constraint image is improved effectively. The time consuming is short, and it has some advantages
Brain MR Image Segmentation Based on an Adaptive Combination of Global and Local Fuzzy Energy
This paper presents a novel fuzzy algorithm for segmentation of brain MR images and simultaneous estimation of intensity inhomogeneity. The proposed algorithm defines an objective function including a local fuzzy energy and a global fuzzy energy. Based on the assumption that the local image intensities belonging to each different tissue satisfy Gaussian distributions with different means, we derive the local fuzzy energy by utilizing maximum a posterior probability (MAP) and Bayes rule. The global fuzzy energy is defined by measuring the distance between the original image and the corresponding inhomogeneity-free image. We combine the global fuzzy energy with the local fuzzy energy using an adaptive weight function whose value varies with the local contrast of the image. This combination enables the proposed algorithm to address intensity inhomogeneity and to improve the accuracy of segmentation and its robustness to initialization. Besides, the proposed algorithm incorporates neighborhood spatial information into the membership function to reduce the impact of noise. Experimental results for synthetic and real images validate the desirable performances of the proposed algorithm
Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images.
We compared the efficacy of three automated brain injury detection methods, namely symmetry-integrated region growing (SIRG), hierarchical region splitting (HRS) and modified watershed segmentation (MWS) in human and animal magnetic resonance imaging (MRI) datasets for the detection of hypoxic ischemic injuries (HIIs). Diffusion weighted imaging (DWI, 1.5T) data from neonatal arterial ischemic stroke (AIS) patients, as well as T2-weighted imaging (T2WI, 11.7T, 4.7T) at seven different time-points (1, 4, 7, 10, 17, 24 and 31 days post HII) in rat-pup model of hypoxic ischemic injury were used to assess the temporal efficacy of our computational approaches. Sensitivity, specificity, and similarity were used as performance metrics based on manual ('gold standard') injury detection to quantify comparisons. When compared to the manual gold standard, automated injury location results from SIRG performed the best in 62% of the data, while 29% for HRS and 9% for MWS. Injury severity detection revealed that SIRG performed the best in 67% cases while 33% for HRS. Prior information is required by HRS and MWS, but not by SIRG. However, SIRG is sensitive to parameter-tuning, while HRS and MWS are not. Among these methods, SIRG performs the best in detecting lesion volumes; HRS is the most robust, while MWS lags behind in both respects
A Geometric Flow Approach for Segmentation of Images with Inhomongeneous Intensity and Missing Boundaries
Image segmentation is a complex mathematical problem, especially for images
that contain intensity inhomogeneity and tightly packed objects with missing
boundaries in between. For instance, Magnetic Resonance (MR) muscle images
often contain both of these issues, making muscle segmentation especially
difficult. In this paper we propose a novel intensity correction and a
semi-automatic active contour based segmentation approach. The approach uses a
geometric flow that incorporates a reproducing kernel Hilbert space (RKHS) edge
detector and a geodesic distance penalty term from a set of markers and
anti-markers. We test the proposed scheme on MR muscle segmentation and compare
with some state of the art methods. To help deal with the intensity
inhomogeneity in this particular kind of image, a new approach to estimate the
bias field using a fat fraction image, called Prior Bias-Corrected Fuzzy
C-means (PBCFCM), is introduced. Numerical experiments show that the proposed
scheme leads to significantly better results than compared ones. The average
dice values of the proposed method are 92.5%, 85.3%, 85.3% for quadriceps,
hamstrings and other muscle groups while other approaches are at least 10%
worse.Comment: Presented at CVIT 2023 Conference. Accepted to Journal of Image and
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