Global Intensity Variations Research Articles

Robust cell segmentation based on gradient detection, Gabor filtering and morphological erosion

Research on cell segmentation is experiencing growing pains that need to be addressed for developing generic and robust techniques. The diversity of cells is growing rapidly and the demand for segmenting different types of cell images continue to increase in recent years. It is still very challenging for existing methods to segment various types of cells automatically and robustly. In this paper, we try to address this challenge by proposing an approach that is capable of segmenting various types of cells robustly. To avoid the effect of the global intensity variations, the gradients of cells are computed and then smoothed by the Gabor filter to generate the gradient image with increased intensity uniformity. To find the optimal method for cell foreground segmentation, we evaluated and compared state of the art threshold selection methods extensively. The slope difference distribution (SDD) method was testified as the optimal threshold selection method for cell segmentation and its optimal parameters were obtained in this paper based on a variety of cell images. We tested several morphological erosion methods and combined the iterative erosion method and the area-constrained ultimate erosion method to separate the connected cells robustly. Thirteen types of cells were used to compare the proposed approach with state of the art approaches. The quantitative comparison showed that the proposed approach achieved the highest average F-measure accuracy, 95.61 %. Thus, the proposed approach has the potential to be useful for various microscopic applications.

A generic approach for cell segmentation based on Gabor filtering and area-constrained ultimate erosion.

Nowadays, the demand for segmenting different types of cells imaged by microscopes is increased tremendously. The requirements for the segmentation accuracy are becoming stricter. Because of the great diversity of cells, no traditional methods could segment various types of cells with adequate accuracy. In this paper, we aim to propose a generic approach that is capable of segmenting various types of cells robustly and counting the total number of cells accurately. To this end, we utilize the gradients of cells instead of intensity for cell segmentation because the gradients are less affected by the global intensity variations. To improve the segmentation accuracy, we utilize the Gabor filter to increase the intensity uniformity of the gradient image. To get the optimal segmentation, we utilize the slope difference distribution based threshold selection method to segment the Gabor filtered gradient image. At last, we propose an area-constrained ultimate erosion method to separate the connected cells robustly. Twelve types of cells are used to test the proposed approach in this paper. Experimental results showed that the proposed approach is very promising in meeting the strict accuracy requirements for many applications.

Dynamic PET reconstruction using temporal patch-based low rank penalty for ROI-based brain kinetic analysis

Dynamic positron emission tomography (PET) is widely used to measure changes in the bio-distribution of radiopharmaceuticals within particular organs of interest over time. However, to retain sufficient temporal resolution, the number of photon counts in each time frame must be limited. Therefore, conventional reconstruction algorithms such as the ordered subset expectation maximization (OSEM) produce noisy reconstruction images, thus degrading the quality of the extracted time activity curves (TACs). To address this issue, many advanced reconstruction algorithms have been developed using various spatio-temporal regularizations. In this paper, we extend earlier results and develop a novel temporal regularization, which exploits the self-similarity of patches that are collected in dynamic images. The main contribution of this paper is to demonstrate that the correlation of patches can be exploited using a low-rank constraint that is insensitive to global intensity variations. The resulting optimization framework is, however, non-Lipschitz and non-convex due to the Poisson log-likelihood and low-rank penalty terms. Direct application of the conventional Poisson image deconvolution by an augmented Lagrangian (PIDAL) algorithm is, however, problematic due to its large memory requirements, which prevents its parallelization. Thus, we propose a novel optimization framework using the concave-convex procedure (CCCP) by exploiting the Legendre–Fenchel transform, which is computationally efficient and parallelizable. In computer simulation and a real in vivo experiment using a high-resolution research tomograph (HRRT) scanner, we confirm that the proposed algorithm can improve image quality while also extracting more accurate region of interests (ROI) based kinetic parameters. Furthermore, we show that the total reconstruction time for HRRT PET is significantly accelerated using our GPU implementation, which makes the algorithm very practical in clinical environments.

Techniques to derive geometries for image-based Eulerian computations.

The performance of three frequently used level set-based segmentation methods is examined for the purpose of defining features and boundary conditions for image-based Eulerian fluid and solid mechanics models. The focus of the evaluation is to identify an approach that produces the best geometric representation from a computational fluid/solid modeling point of view. In particular, extraction of geometries from a wide variety of imaging modalities and noise intensities, to supply to an immersed boundary approach, is targeted. Two- and three-dimensional images, acquired from optical, X-ray CT, and ultrasound imaging modalities, are segmented with active contours, k-means, and adaptive clustering methods. Segmentation contours are converted to level sets and smoothed as necessary for use in fluid/solid simulations. Results produced by the three approaches are compared visually and with contrast ratio, signal-to-noise ratio, and contrast-to-noise ratio measures. While the active contours method possesses built-in smoothing and regularization and produces continuous contours, the clustering methods (k-means and adaptive clustering) produce discrete (pixelated) contours that require smoothing using speckle-reducing anisotropic diffusion (SRAD). Thus, for images with high contrast and low to moderate noise, active contours are generally preferable. However, adaptive clustering is found to be far superior to the other two methods for images possessing high levels of noise and global intensity variations, due to its more sophisticated use of local pixel/voxel intensity statistics. It is often difficult to know a priori which segmentation will perform best for a given image type, particularly when geometric modeling is the ultimate goal. This work offers insight to the algorithm selection process, as well as outlining a practical framework for generating useful geometric surfaces in an Eulerian setting.

Robust face detection using local gradient patterns and evidence accumulation

This paper proposes a novel face detection method using local gradient patterns (LGP), in which each bit of the LGP is assigned the value one if the neighboring gradient of a given pixel is greater than the average of eight neighboring gradients, and 0 otherwise. LGP representation is insensitive to global intensity variations like the other representations such as local binary patterns (LBP) and modified census transform (MCT), and to local intensity variations along the edge components. We show that LGP has a higher discriminant power than LBP in both the difference between face histogram and non-face histogram and the detection error based on the face/face distance and face/non-face distance. We also reduce the false positive detection error greatly by accumulating evidences from multi-scale detection results with negligible extra computation time. In experiments using the MIT+CMU and FDDB databases, the proposed LGP-based face detection followed by evidence accumulation method provides a face detection rate that is 5–27% better than those of existing methods, and reduces the number of false positives greatly.

Exploring the influence of the non-dipole field on magnetic records for field reversals and excursions

SUMMARY We have used the model CALS7K.2 to explore the possible influence of the time-varying non-dipole components of the geomagnetic field during field reversals and excursions. Our findings suggest that non-dipole components could add significant structure to the field during the reversal and excursion processes. Globally, the main polarity reversal is variable in duration and rapid reversals on subdecadal timescales are seen for a small number of locations. The model generates variable reversal paths; however, there is a longitudinal preference both spatially and, more weakly, temporally. Directional reversal features are not globally synchronous: some polarity changes finish before they start elsewhere. Global intensity variations, however, appear more coherent. We also find support for the idea that field intensity changes occur some time before and after the major directional changes of the reversal. Large excursions appear naturally when the axial dipole has been reduced to 20 per cent for the whole time period; however, they are not globally synchronous or uniform.

Correction of slab boundary artifact using histogram matching.

To improve the imaging efficiency of multi-slab three-dimensional acquisitions by decreasing slab overlap and the number of blanked slices. Application of the histogram matching technique for correction of the slab boundary artifact (SBA) due to non-ideal radiofrequency pulse profiles has been proposed. The technique was validated by studying its effectiveness in reducing the SBA in phantom images, as well as in proton density- and T2-weighted brain images. The correction algorithm considerably decreases the disruption of image volume continuity due to the SBA, and improves slice-to-slice intensity continuity. The proposed technique can greatly reduce the SBA when the SBA can be described as a global intensity variation along the slab. The technique may be used to improve imaging efficiency of the multi-slab three-dimensional fast spin-echo sequences by decreasing the amount of required slab overlap.

Geomagnetic intensity variations over the past 780 kyr obtained from near-seafloor magnetic anomalies.

Knowledge of past variations in the intensity of the Earth's magnetic field provides an important constraint on models of the geodynamo. A record of absolute palaeointensity for the past 50 kyr has been compiled from archaeomagnetic and volcanic materials, and relative palaeointensities over the past 800 kyr have been obtained from sedimentary sequences. But a long-term record of geomagnetic intensity should also be carried by the thermoremanence of the oceanic crust Here we show that near-seafloor magnetic anomalies recorded over the southern East Pacific Rise are well correlated with independent estimates of geomagnetic intensity during the past 780 kyr. Moreover, the pattern of absolute palaeointensity of seafloor glass samples from the same area agrees with the well-documented dipole intensity pattern for the past 50 kyr. A comparison of palaeointensities derived from seafloor glass samples with global intensity variations thus allows us to estimate the ages of surficial lava flows in this region. The record of geomagnetic intensity preserved in the oceanic crust should provide a higher-time-resolution record of crustal accretion processes at mid-ocean ridges than has previously been obtainable.