Multidirectional focus measure for accurate three-dimensional shape recovery of microscopic objects.

Shape from focus (SFF) is a technique to recover the shape of an object from multiple images taken at various focus settings. Most of conventional SFF techniques compute focus value of a pixel by applying one of focus measure operators on neighboring pixels on the same image frame. However, in the optics with limited depth of field, neighboring pixels of an image have different degree of focus for curved objects, thus the computed focus value does not reflect the accurate focus level of the pixel. Ideally, an accurate focus value of a pixel needs to be measured from the neighboring pixels lying on tangential plane of the pixel in image space. In this article, a tangential plane on each pixel location (i, j) in image sensor is searched by selecting one of five candidate planes based on the assumption that the maximum variance of focus values along the optical axis is achieved from the neighborhood lying on tangential plane of the pixel (i, j). Then, a focus measure operator is applied on neighboring pixels lying on the searched plane. The experimental results on both the synthetic and real microscopic objects show the proposed method produces more accurate three-dimensional shape in comparison to conventional SFF method that applies focus measures on original image planes.

A hybrid model for forecasting of particulate matter concentrations based on multiscale characterization and machine learning techniques.

Accurate prediction of particulate matter (PM) using time series data is a challenging task. The recent advancements in sensor technology, computing devices, nonlinear computational tools, and machine learning (ML) approaches provide new opportunities for robust prediction of PM concentrations. In this study, we develop a hybrid model for forecasting PM10 and PM2.5 based on the multiscale characterization and ML techniques. At first, we use the empirical mode decomposition (EMD) algorithm for multiscale characterization of PM10 and PM2.5 by decomposing the original time series into numerous intrinsic mode functions (IMFs). Different individual ML algorithms such as random forest (RF), support vector regressor (SVR), k-nearest neighbors (kNN), feed forward neural network (FFNN), and AdaBoost are then used to develop EMD-ML models. The air quality time series data from Masfalah air station Makkah, Saudi Arabia are utilized for validating the EMD-ML models, and results are compared with non-hybrid ML models. The PMs (PM10 and PM2.5) concentrations data of Dehli, India are also utilized for validating the EMD-ML models. The performance of each model is evaluated using root mean square error (RMSE) and mean absolute error (MAE). The average bias in the predictive model is estimated using mean bias error (MBE). Obtained results reveal that EMD-FFNN model provides the lowest error rate for both PM10 (RMSE = 12.25 and MAE = 7.43) and PM2.5 (RMSE = 4.81 and MAE = 3.02) using Misfalah, Makkah data whereas EMD-kNN model provides the lowest error rate for PM10 (RMSE = 20.56 and MAE = 12.87) and EMD-AdaBoost provides the lowest error rate for PM2.5 (RMSE = 15.29 and MAE = 9.45) using Dehli, India data. The findings also reveal that EMD-ML models can be effectively used in forecasting PM mass concentrations and to develop rapid air quality warning systems.

Mitosis detection in breast cancer histopathology images using hybrid feature space.

Breast Cancer grading is a challenging task as regards image analysis, which is normally based on mitosis count rate. The mitotic count provides an estimate of aggressiveness of the tumor. The detection of mitosis is a challenging task because in a frame of slides at X40 magnification, there are hundreds of nuclei containing few mitotic nuclei. However, manual counting of mitosis by pathologists is a difficult and time intensive job, moreover conventional method rely mainly on the shape, color, and/or texture features as well as pathologist experience. The objective of this study is to accept the atypaia-2014 mitosis detection challenge, automate the process of mitosis detection and a proposal of a hybrid feature space that provides better discrimination of mitotic and non-mitotic nuclei by combining color features with morphological and texture features. To exploit color channels, they were first selected, and then normalized and cumulative histograms were computed in wavelet domain. A detailed analysis presented on these features in different color channels of respective color spaces using Random Forest (RF) and Support Vector Machine (SVM) classifiers. The proposed hybrid feature space when used with SVM classifier achieved a detection rate of 78.88% and F-measure of 72.07%. Our results, especially high detection rate, indicate that proposed hybrid feature space model contains discriminant information for mitotic nuclei, being therefore a very capable are for exploration to improve the quality of the diagnostic assistance in histopathology.

Estimation of gamma-corrected exposure time ratio in multi-exposure images for removal of moving objects.

The constructed high-dynamic-range image from merging standard low-dynamic-range images with different camera exposures contains ghost-like artifacts caused by moving objects in the scene. We present a method to utilize the gamma-corrected exposure time ratio between multi-exposure images for removal of moving objects. Between each consecutive image pair in multi-exposure images, the ratio of their exposure times is computed and raised to the power gamma, and this value is used as a cue to detect the pixels corresponding to the moving objects. We propose a method to estimate this ratio from the observed image intensity values, in case the exposure time information or gamma value is unknown. Then the moving objects in multi-exposure images are removed by replacing the intensity values of the detected moving pixels with their expected background values. Experimental results show that the proposed method could remove fast-moving objects from the original multi-exposure images and construct a ghost-free high-dynamic-range image.

Tracking of EEG activity using motion estimation to understand brain wiring.

The fundamental step in brain research deals with recording electroencephalogram (EEG) signals and then investigating the recorded signals quantitatively. Topographic EEG (visual spatial representation of EEG signal) is commonly referred to as brain topomaps or brain EEG maps. In this chapter, full search full search block motion estimation algorithm has been employed to track the brain activity in brain topomaps to understand the mechanism of brain wiring. The behavior of EEG topomaps is examined throughout a particular brain activation with respect to time. Motion vectors are used to track the brain activation over the scalp during the activation period. Using motion estimation it is possible to track the path from the starting point of activation to the final point of activation. Thus it is possible to track the path of a signal across various lobes.

Searching surface orientation of microscopic objects for accurate 3D shape recovery.

In this article, we propose a new shape from focus (SFF) method to estimate 3D shape of microscopic objects using surface orientation cue of each object patch. Most of the SFF algorithms compute the focus value of a pixel from the information of neighboring pixels lying on the same image frame based on an assumption that the small object patch corresponding to the small neighborhood of a pixel is a plane parallel to the focal plane. However, this assumption fails in the optics with limited depth of field where the neighboring pixels of an image have different degree of focus. To overcome this problem, we try to search the surface orientation of the small object patch corresponding to each pixel in the image sequence. Searching of the surface orientation is done indirectly by principal component analysis. Then, the focus value of each pixel is computed from the neighboring pixels lying on the surface perpendicular to the corresponding surface orientation. Experimental results on synthetic and real microscopic objects show that the proposed method produces more accurate 3D shape in comparison to the existing techniques.

Depth from focus based on combinatorial optimization.

Depth from focus (DFF) is a technique to estimate the depth and 3D shape of an object from a sequence of images obtained at different focus settings. The DFF is presented as a combinatorial optimization problem. After the estimate of the initial depth map solution of an object, the algorithm updates the depth map iteratively from the specially defined neighborhood. The results of the proposed DFF algorithm have shown significant improvements in both the accuracy of the depth map estimation and the computational complexity, with respect to the existing DFF methods.

Accurate shape from focus based on focus adjustment in optical microscopy.

Optical microscopy allows a magnified view of the sample while decreasing the depth of focus. Although the acquired images from limited depth of field have both blurred and focused regions, they can provide depth information. The technique to estimate the depth and 3D shape of an object from the images of the same sample obtained at different focus settings is called shape from focus (SFF). In SFF, the measure of focus--sharpness--is the crucial part for final 3D shape estimation. The conventional methods compute sharpness by applying focus measure operator on each 2D image frame of the image sequence. However, such methods do not reflect the accurate focus levels in an image because the focus levels for curved objects require information from neighboring pixels in the adjacent frames too. To address this issue, we propose a new method based on focus adjustment which takes the values of the neighboring pixels from the adjacent image frames that have approximately the same initial depth as of the center pixel and then it re-adjusts the center value accordingly. Experiments were conducted on synthetic and microscopic objects, and the results show that the proposed technique generates better shape and takes less computation time in comparison with previous SFF methods based on focused image surface (FIS) and dynamic programming.