Equal-sized Squares Research Articles

OP-convNet: A Patch Classification-Based Framework for CT Vertebrae Segmentation

Accurate vertebrae segmentation from medical images plays an important role in clinical tasks of surgical planning, diagnosis, kyphosis, scoliosis, degenerative disc disease, spondylolisthesis, and post-operative assessment. Although the structures of bone have high contrast in medical images, vertebrae segmentation is a challenging task due to its complex structure, abnormal spine curves, and unclear boundaries. In recent years, deep learning has been widely applied in the segmentation of vertebrae images. In this paper, towards a robust and automatic segmentation system, we present an overlapping patch-based convNet (OP-convNet) model for automatic vertebrae CT images segmentation. Due to the greater memory and processing costs associated with 3D convolutional neural networks, as well as the risk of over-fitting, we employ overlapping patches in segmentation tasks using 2D convNet. In the proposed vertebrae segmentation method, OP-convNet effectively keeps the local information contained in CT images. We divide CT image slices into equal-sized square overlapping patches and applied the RUS-function on these patches for class balancing to minimize computational requirements. Then, these patches are input into the model along with their corresponding ground truth patches. This method has been evaluated on publicly available CT images from the MICCAI CSI workshop challenge. The results indicate that OP-convNet has precision (PRE) of 90.1%, specificity (SPE) of 99.4%, accuracy (ACC) of 98.8%, F-score of 90.1% in terms of the patch-based classification accuracy, and BF-score of 90.2%, sensitivity (SEN) of 90.3%, Jaccard index (JAC) of 82.3%, dice similarity score (DSC) of 89.9% in terms of the segmentation accuracy that outperform previous methods across all metrics.

Stationary Tetrolet Transform: an Improved Algorithm for Tetrolet Transform

In order to get an efficient image multi-scale geometrical representation, the basic principle of tetrolet transform is studied and an efficient stationary tetrolet transform algorithm based on Haar wavelet transform is proposed. Tetrolets are Haar-type wavelets whose supports are tetrominoes which are shapes made by connecting four equal-sized squares. Tetrolet transform is divide the image into 4 × 4 blocks in the horizontal and vertical direction, there is no overlap between the sub-blocks. Because there is no overlap between the images blocks, so the image decomposition coefficients in image processing is easy produce Gibbs phenomenon. Stationary tetrolet transform is a new adaptive Haar-type wavelet transforms which the block overlaps method in image decomposition process is inserted into the middle of the original coefficients in tetrolet transform. The corresponding filter bank algorithm is simple but enormously effective. Compare with standard two dimensional wavelet transform, stationary tetrolet transform is a novel tetrominoes based multi-scale geometrical transform tool, which can capture image anisotropic geometrical structures information efficiently by multi-direction selection. In this paper, the decomposition and reconstruction algorithms of the stationary tetrolet transform are described in detail, and the simulation and analysis of the decomposition of the image using the stationary tetrolet transform is carried out. Experimental results show that compared with traditional algorithm, the proposed algorithm can get better sparse representation and eliminate the blocking artifacts in image processing resulted from tetrolet transform algorithm.

Wake-induced vibration interference between a fixed square cylinder and a 2-DOF downstream square cylinder at low Reynolds numbers

Wake-induced vibration (WIV) of a two-degree-of-freedom (2-DOF) downstream square cylinder behind a stationary equal-size upstream square cylinder is numerically investigated at low Reynolds numbers by using Characteristics-Based Split (CBS) finite element algorithm. Due to the interference between the cylinders, the flow pattern and dynamic characteristics of become more complex than an isolated cylinder case. The spacing ratio of the two equal-sized square cylinders in the tandem arrangement is fixed as L/D = 5.0. The Reynolds numbers of this two-cylinder system have five types, varying between 40 and 200 with a unit step of △Re = 40. The reduced mass of the square cylinder is Mr = 2.0, while its reduced velocity changes between Ur = 3.0–18.0. The numerical results show that the reduced velocity and Reynolds number can affect the characteristics of the flow patterns, oscillatory frequency, maximum amplitudes, and X-Y trajectories of the downstream square cylinder. The predominant vortex shedding patterns are 2S, 2S*, 2P, P + S, 2T and steady mode. Additionally, the figure “8” and figure “dual-8” are observed in the X-Y vibrating trajectories of the downstream square cylinder. Finally, the interactions between cylinders underlying the vibration characteristics of the square cylinder behind a stationary square cylinder with different Reynolds numbers are revealed.

A Trajectory Data Clustering Method Based On Dynamic Grid Density

Under the traditional method of frequent trajectory mining, the location of data is obtained through the GPS device. However, limited equipment accuracy may incur location ambiguity. In this paper, we propose a new trajectory data clustering method based on dynamic grid density, in order to remove this ambiguity. In this method, the trajectory space of an object is firstly divided into equal-sized squares dynamically. Then the trajectories of object are mapped to their corresponding square. Next, the density of each grid is calculated and all the frequent squares are acquired given the minimum support threshold. Lastly, the frequent area is obtained by merging the frequent squares acquired previously, using the boundary function provided. The experimental results show that this method provides an optional way of finding the frequent movement sequence.

Research on Polarization Image Fusion Using Tetrolet Transform

Tetrolets are Haar-type wavelets whose supports are tetrominoes which are shapes made by connecting four equal-sized squares. Firstly, the magnitude of polarization image and angle of polarization image can be decomposed into low-frequency coefficients and high-frequency coefficients with multi-scales and multi-directions using tetrolet transform. For the low-frequency coefficients, the average fusion method is used. For the each directional high frequency sub-band coefficients, the larger value of region energy information measurement is used to select the better coefficients for fusion. At last the fused image can be obtained by utilizing inverse transform for fused tetrolet coefficients. Experimental results show that the proposed method can detect image features more effectively and the fused image has better subjective visual effect.

The easy block Hadamard transform: A new adaptive directional Hadamard transform for sparse image representation

In order to get an efficient image representation, we introduce a new locally adaptive Hadamard transform, called Easy Block Hadamard Transform (EBHT). EBHT is a type of directional Hadamard transform, which can be adapted to the image geometry in each block. Firstly, the image is divided into eight equal-sized squares and the adaptive directional Hadamard filter is employed to get the low-pass data and high-pass data. In the further level of the filter bank algorithm, we just divide the low-pass image into four equal-sized squares. EBHT is very simple, but effective. Numerical results show the excellent efficiency of the EBHT for sparse image representation.

Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSA

Landslide detection from extensive remote-sensing imagery is an important preliminary work for landslide mapping, landslide inventories, and landslide hazard assessment. Aimed at development of an automatic procedure for landslide detection, a new method for automatic landslide detection from remote-sensing imagery is presented in this study. We achieved this objective using a scene classification method based on the bag-of-visual-words (BoVW) representation in combination with the unsupervised probabilistic latent semantic analysis (pLSA) model and the k-nearest neighbour (k-NN) classifier. Given a remote-sensing image, we divided it into equal-sized square sub-images and then described each sub-image as a BoVW representation. The pLSA model was applied to sub-images by using the BoVW representation to discover the object classes depicted in the sub-images, and then a k-NN classifier was used to classify the sub-images into landslide areas and non-landslide areas based on object distribution. We investigated the performance and applicability of the method using remote-sensing imagery from the Ili area. The results show that the method is robust and can produce good performance without the acquisition of three-dimensional (3D) topography. We anticipate that these results will be helpful in landslide inventory mapping and landslide hazard assessment in landslide-stricken areas.

The Critical Grid Size and Transmission Radius for Local-Minimum-Free Grid Routing in Wireless Ad Hoc and Sensor Networks

In grid routing, the plane is tessellated into equal-sized square cells. Two cells are called neighbor cells if they share a common edge, and two nodes are called routing neighbors if they are in neighbor cells and within each other's transmission range. If communication parties are in the same cell, packets can be transmitted directly; otherwise, packets are forwarded to routing neighbors that are in cells closer to destination cells. As a greedy strategy, grid routing suffers the existence of local minima at which no neighbor nodes exist for relaying packets. To guarantee deliverability, in this paper, we investigate two vital parameters of grid routing, called the grid size and the transmission radius. Assume that nodes are represented by a Poisson point process with rate n over a unit-area square, and let l denote the grid size and r the transmission radius. First, we show that if for some constant β and r = √5l, then β = 1 is the threshold for deliverability. In other words, there almost surely do not exist local minima if β > 1 and there almost surely exist local minima if β 1, we give sufficient and necessary conditions to determine the critical transmission radius (CTR) for deliverability. Then, we show that as β ≅ 1.092, the CTR is the minimum over all β > 1. Simulation results are given to validate this theoretical work.

Tetrolet transform: A new adaptive Haar wavelet algorithm for sparse image representation

In order to get an efficient image representation we introduce a new adaptive Haar wavelet transform, called Tetrolet Transform. Tetrolets are Haar-type wavelets whose supports are tetrominoes which are shapes made by connecting four equal-sized squares. The corresponding fast filter bank algorithm is simple but very effective. In every level of the filter bank algorithm we divide the low-pass image into 4 × 4 blocks. Then in each block we determine a local tetrolet basis which is adapted to the image geometry in this block. An analysis of the adaptivity costs leads to modified versions of our method. Numerical results show the strong efficiency of the tetrolet transform for image approximation.

Computing a threaded quadtree from the Delaunay triangulation in linear time

The quadtree in this paper is a variant in which two nodes are threaded (linked) if they represent equal-sized squares that are sufficiently close to each other. Given the Delaunay triangulation, i...

Design of tool magazine and operational areas for a multi-tool machine

This paper presents algorithms for designing both the machine operational area and the tool magazine of a multi-tool machine with the objective of minimizing the tool interchange device (robot) travel time. The problem is treated as a machine interference problem with a machine layout problem appended to it. It is shown that the design problem is computationally easy (the computational requirements are a polynomial function of the number of operations considered) when the machine working areas are equal-sized and the tool slots on the magazine are also equal-sized squares. On the other hand, it is also shown that the design problem is computationally difficult (the computational requirements are an exponential function of the number of operations considered) when either the machine working areas or the tool slots on the magazine are rectangles. In either case the total number of required computations has an upper bound.