Skeleton Operation Research Articles

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Cell polarity is a macroscopic phenomenon established by a collection of spatially concentrated molecules and structures that culminate in the emergence of specialized domains at the subcellular level. It is associated with developing asymmetric morphological structures that underlie key biological functions such as cell division, growth, and migration. In addition, the disruption of cell polarity has been linked to tissue-related disorders such as cancer and gastric dysplasia. Current methods to evaluate the spatiotemporal dynamics of fluorescent reporters in individual polarized cells often involve manual steps to trace a midline along the cells' major axis, which is time consuming and prone to strong biases. Furthermore, although ratiometric analysis can correct the uneven distribution of reporter molecules using two fluorescence channels, background subtraction techniques are frequently arbitrary and lack statistical support. This manuscript introduces a novel computational pipeline to automate and quantify the spatiotemporal behavior of single cells using a model of cell polarity: pollen tube/root hair growth and cytosolic ion dynamics. A three-step algorithm was developed to process ratiometric images and extract a quantitative representation of intracellular dynamics and growth. The first step segments the cell from the background, producing a binary mask through a thresholding technique in the pixel intensity space. The second step traces a path through the midline of the cell through a skeletonization operation. Finally, the third step provides the processed data as a ratiometric timelapse and yields a ratiometric kymograph (i.e., a 1D spatial profile through time). Data from ratiometric images acquired with genetically encoded fluorescent reporters from growing pollen tubes were used to benchmark the method. This pipeline allows for faster, less biased, and more accurate representation of the spatiotemporal dynamics along the midline of polarized cells, thus advancing the quantitative toolkit available to investigate cell polarity. The AMEBaS Python source code is available at: https://github.com/badain/amebas.git

A Morphological Image Segmentation Algorithm for Circular Overlapping Cells

Cell segmentation is an important topic in medicine. A cell image segmentation algorithm based on morphology is proposed. First, some morphological operations, including top-hat transformation, bot-hat transformation, erosion operation, dilation operation, opening operation, closing operation, majority operation, skeleton operation, etc., are applied to remove noise or enhance cell images. Then the small blocks in the cell image are deleted as noise, the medium blocks are removed and saved as normal cells, and the large blocks are segmented as overlapping cells. Each point on the edge of the overlapping cell area to be divided is careful checked. If the shape of the surrounding area is a corner and its angle is smaller than the specified value, the overlapping cell will be divided along the midline of the corner. The length of each division is about a quarter of the diameter of a normal cell. Then small blocks are deleted, and medium blocks are removed and saved, after the edges of all blocks are smoothed. This step is repeated until no dividing point is found. The last remaining image, plus the saved blocks, is the final segmentation result of the cell image. The experimental results show that this algorithm has high segmentation accuracy for lightly or moderately overlapping cells.

Application of Axis Orbit Image Optimization in Fault Diagnosis for Rotor System

The shape characteristic of the axis orbit plays an important role in the fault diagnosis of rotating machinery. However, the original signal is typically messy, and this affects the identification accuracy and identification speed. In order to improve the identification effect, an effective fault identification method for a rotor system based on the axis orbit is proposed. The method is a combination of ensemble empirical mode decomposition (EEMD), morphological image processing, Hu invariant moment feature vector, and back propagation (BP) neural network. Experiments of four fault forms are performed in single-span rotor and double-span rotor test rigs. Vibration displacement signals in the X and Y directions of the rotor are processed via EEMD filtering to eliminate the high-frequency noise. The mathematical morphology is used to optimize the axis orbit including the dilation and skeleton operation. After image processing, Hu invariant moments of the skeleton axis orbits are calculated as the feature vector. Finally, the BP neural network is trained to identify the faults of the rotor system. The experimental results indicate that the time of identification of the tested axis orbits via morphological processing corresponds to 13.05 s, and the identification accuracy rate ranges to 95%. Both exceed that without mathematical morphology. The proposed method is reliable and effective for the identification of the axis orbit and aids in online monitoring and automatic identification of rotor system faults.

Three Dimensional Image Processing in Hexagonal Prism Lattice of Z3 Grid

A 2D hexagonal image is an array of what are called pixels which are the coordinates of the hexagonal lattice points decided by the linear horizontal rows and the nonlinear vertical zig-zags. A 2D hexagonal image could also be informally called as image slice or a matrix of pixel values arranged in a hexagonal array. A 3D hexagonal image is viewed as an ordered sequence of 2D hexagonal image slices arranged in the z-direction and the 3D arrangement of voxel values is called as a prism of voxel values. Most of the 3D hexagonal image processing operations are similar to those of 2D hexagonal image processing. 3D hexagonal images are processed with the help of 3D hexagonal scanning windows, whereas 2D hexagonal images are processed with the help of 2D hexagonal scanning windows. For instance, a 3D hexagonal image processing operation like 3D surface detection is carried out using analogous 2D edge detection algorithm on every image slice and the processed slices assembled to visualize 3D surface detected image. In fact, 2D contours of an image slice are called superficial features and closed surfaces of a 3D image are called volumetric features. One can always obtain surface detected version of a 3D hexagonal image by processing the 2D hexagonal image slices using 2D edge detection operation, and consequently the 3D surface detection operation is termed as 2.5D hexagonal image processing. One could also process the 3D hexagonal image data using a 3D surface detection algorithm, in which case it is termed as 3D hexagonal image processing. This is not the case with the operation of skeltonization. One cannot make use of 2.5D skeletonization operation of 2D image slices in order to get skeltonized version of the corresponding 3D image. In fact, one would come across discrepancies and differences when 2.5D skeletonization of 2D hexagonal image slices of a 3D hexagonal image is carried out instead of the direct 3D skeletonization of the 3D hexagonal image. This paper highlights certain 3D algorithms for processing 3D hexagonal images.

Development of airframe design elements control technique under operational conditions

The paper proposes to use a method of infrared pulse-echo inspection to control airframe designelements, made of polymeric composite materials under operators` conditions. The following is described:skeleton operation diagram of the given method, device structure and results of the preliminary researchesconfirming operation capacity of the method offered

MRI-based segmentation of pubic bone for evaluation of pelvic organ prolapse.

Pelvic organ prolapse (POP) is a major women's health problem. Its diagnosis through magnetic resonance imaging (MRI) has become popular due to current inaccuracies of clinical examination. The diagnosis of POP on MRI consists of identifying reference points on pelvic bone structures for measurement and evaluation. However, it is currently performed manually, making it a time-consuming and subjective procedure. We present a new segmentation approach for automating pelvic bone point identification on MRI. It consists of a multistage mechanism based on texture-based block classification, leak detection, and prior shape information. Texture-based block classification and clustering analysis using K-means algorithm are integrated to generate the initial bone segmentation and to identify leak areas. Prior shape information is incorporated to obtain the final bone segmentation. Then, the reference points are identified using morphological skeleton operation. Results demonstrate that the proposed method achieves higher bone segmentation accuracy compared to other segmentation methods. The proposed method can also automatically identify reference points faster and with more consistency compared with the manually identified point process by experts. This research aims to enable faster and consistent pelvic measurements on MRI to facilitate and improve the diagnosis of female POP.

Image based measurements for evaluation of pelvic organ prolapse

Magnetic resonance imaging (MRI) measurements are essential for the diagnosis of pelvic organ prolapse given the inaccuracy of clinical examination. However, MRI pelvic floor measurements are currently performed manually and can be inconsistent and time-consuming. In this paper, we present a scheme for semi-automatic measurement modeling on MRI based on image segmentation and intersecting point identification methods. The segmentation algorithm is a multi-stage mechanism based on block grouping, support vector machine classification, morphological operation and prior shape information. Block grouping is achieved by classifying blocks as bone or background based on image texture features. The classified blocks are then used to find the initial segmentation by the first phase morphological opening. Prior shape information is incorporated into the initial segmentation to obtain the final segmentation using registration with the similarity type transformation. After segmentation, points of reference that are used for pelvic floor measurements are identified using morphological skeleton operation. The experiments on the MRI images show that the presented scheme can detect the points of reference on the pelvic floor structure to determine the reference lines needed for the assessment of pelvic organ prolapse. This will lead towards more consistent and faster pelvic organ prolapse diagnosis on dynamic MRI studies, and possible screening procedures for predicting predisposition to pelvic organ prolapse by radiologic evaluation of pelvic floor measurements.

A Survey On Various Approaches Of Text Extraction In Images

Text Extraction plays a major role in finding vital and valuable information. Text extraction involves detection, localization, tracking, binarization, extraction, enhancement and recognition of the text from the given image. These text characters are difficult to be detected and recognized due to their deviation of size, font, style, orientation, alignment, contrast, complex colored, textured background. Due to rapid growth of available multimedia documents and growing requirement for information, identification, indexing and retrieval, many researches have been done on text extraction in images.Several techniques have been developed for extracting the text from an image. The proposed methods were based on morphological operators, wavelet transform, artificial neural network,skeletonization operation,edge detection algorithm, histogram technique etc. All these techniques have their benefits and restrictions. This article discusses various schemes proposed earlier for extracting the text from an image. This paper also provides the performance comparison of several existing methods proposed by researchers in extracting the text from an image.

Automated Measurement of the Arteriolar-to-Venular Width Ratio in Digital Color Fundus Photographs

A decreased ratio of the width of retinal arteries to veins [arteriolar-to-venular diameter ratio (AVR)], is well established as predictive of cerebral atrophy, stroke and other cardiovascular events in adults. Tortuous and dilated arteries and veins, as well as decreased AVR are also markers for plus disease in retinopathy of prematurity. This work presents an automated method to estimate the AVR in retinal color images by detecting the location of the optic disc, determining an appropriate region of interest (ROI), classifying vessels as arteries or veins, estimating vessel widths, and calculating the AVR. After vessel segmentation and vessel width determination, the optic disc is located and the system eliminates all vessels outside the AVR measurement ROI. A skeletonization operation is applied to the remaining vessels after which vessel crossings and bifurcation points are removed, leaving a set of vessel segments consisting of only vessel centerline pixels. Features are extracted from each centerline pixel in order to assign these a soft label indicating the likelihood that the pixel is part of a vein. As all centerline pixels in a connected vessel segment should be the same type, the median soft label is assigned to each centerline pixel in the segment. Next, artery vein pairs are matched using an iterative algorithm, and the widths of the vessels are used to calculate the AVR. We trained and tested the algorithm on a set of 65 high resolution digital color fundus photographs using a reference standard that indicates for each major vessel in the image whether it is an artery or vein. We compared the AVR values produced by our system with those determined by a semi-automated reference system. We obtained a mean unsigned error of 0.06 (SD 0.04) in 40 images with a mean AVR of 0.67. A second observer using the semi-automated system obtained the same mean unsigned error of 0.06 (SD 0.05) on the set of images with a mean AVR of 0.66. The testing data and reference standard used in this study has been made publicly available.

Morphologically extracted trabecular skeleton superimposed upon digital radiograph structure.

The purpose of this study was to employ a morphological filter to digital X-ray images to extract the morphology of trabecular structures in a clearly understandable visual format. This study compares the trabecular skeleton extracted by a morphological filter to the original digital radiographic image by superimposing the images. A morphological filter (a combination of a single structuring element and a skeleton operation) based on a mathematical morphology theory was used to extract the skeletal pattern of trabecular bone from a digital X-ray image of a human femoral neck. Subset images with different operation numbers (n = 1, n = 2) were obtained, and then each image was superimposed on the original digital radiographic image using the superimpose function of a workstation. The extracted trabecular skeleton pattern was fairly consistent with the trabecular structure seen on the digital image according to the opinion of seven dentomaxillofacial radiologists. In their opinion, the majority of the structural elements were reproduced on the extracted skeleton. However, accurate skeleton elements were not extracted in the region of dense trabecular structure. The morphological filter was able to extract a large portion of the bone trabecular structure as a binary skeletal pattern image from trabecular bone on digital X-ray image, but more work is needed to improve the assessment of dense trabeculae.

Comparison of extracted skeleton patterns of human femur and humerus using the line skeleton operation process and skeleton operation process

This study presents and compares trabecular skeletal patterns of human femoral and humeral bone extracted by applying mathematical morphology and morphological filters. Two morphological filters and skeleton processing operations were used to extract the skeleton images. Images obtained with a combination of the single disc-shaped structuring element (5 pixel diameter) and the skeleton operation were compared to images obtained with the line structuring element and the line skeleton operation. Seven dentomaxillofacial radiologists evaluated the resulting images. Line skeleton images (both subset and sumset images) had fewer trabecular elements extracted and higher discontinuity as the operation number increased, and there were obvious differences between the simple skeleton images and the line skeleton images. With more study these differences may be useful in detecting changes in trabecular patterns resulting from disease, stress or altered function.

Skeletal pattern extraction of bone trabeculae using mathematical morphology

Studies of morphology in extracting skeletal paterns of spiculae in mammograms served as a theoretical framework. Using studies of morphology in extraction of skeletal patterns of spiculae in mammograms as a theoretical framework, we applied a morphological filter (a combination of a skeleton operation and a single structuring element) to the extraction of skeletal patterns of bone trabeculae on computed radiographic (CR) images. Then, the comparison of the original images with the extracted skeletal patterns and the relationship between the sequence number(n) of the operation and the extracted skeletal patterns was reviewed. The comparison showed that skeletal patterns of the original image were extracted as binary images when using a morphological filter, and that, when n was small (n=0 or 1), skeletal patterns of both fine and wider bone trabeculae were extracted. Furthermore, with an increase in n, skeletal patterns of fine bone were eliminated, and only wider skeletal patterns were extracted selectively. These findings suggest that development of a suitable morphological filter to extract the skeletal patterns of bone trabeculae may be an effective supplement for computer aided diagnosis of medical images (CADM) in the support of adequate diagnosis of bone diseases.

Initial investigation of mathematical morphology for the digital extraction of the skeletal characteristics of trabecular bone.

To develop a filter utilizing mathematical theory to extract the skeletal patterns of trabecular bone. Studies of morphology in the extraction of patterns of calcification in mammograms provided the theoretical framework. Using these studies as a basis, a morphological filter was applied to extract skeletal patterns from digital images of trabecular bone. Sequential images (subset) were combined in a structured fashion to create an aggregate (sumset) which compared with the original images, skeleton and line skeleton images. Binary images of the skeletal patterns in continuous, round and mesh-like forms were obtained from the original images by processing with the skeleton operation using a disc-shaped single structuring element. The line skeleton operation using line structuring elements with constant directions allowed the extraction of linear and discontinuous patterns. Both the skeleton and line skeleton operations extracted binary subset images which depicted skeletal patterns correlating with the operation sequence. Modification of the morphological filter enhanced the extraction of skeletal characteristics of trabecular bone. A morphological filter may be a useful adjunct in computer-aided structural analysis of bone.

Surgery for atrioventricular node reentry tachycardia

Surgical treatment options for interruption of atrioventricular node reentrant tachycardia include (1) skeletonization of the atrioventricular node by dissecting it from most of its atrial inputs and (2) discrete cryosurgery of the perinodal tissues by applying a series of sequential cryolesions to the atrial tissues immediately adjacent to the atrioventricular node. Both these techniques attempt to interrupt one of the dual atrioventricular node conduction pathways while preserving the other. This report describes 17 consecutive patients who underwent surgical treatment, 10 patients with skeletonization of the atrioventricular node and seven patients with discrete perinodal cryosurgery. There were 10 female and seven male patients and their ages ranged from 28 to 56 years (mean 38). Two of the 17 patients had Wolff-Parkinson-White syndrome and their accessory pathways were interrupted before the atrioventricular nodal reentrant tachycardia was ablated. All the procedures were performed in a normothermic beating heart while atrioventricular conduction was monitored closely. In the skeletonization technique, the right atrial septum was mobilized and the atrioventricular node exposed anterior to the tendon of the Todaro. The perinodal cryosurgical procedure was also performed through a right atriotomy and a series of sequential 3 mm cryolesions were placed around the borders of the triangle of Koch on the inferior right atrial septum. There were no operative deaths. Two patients who underwent the skeletonization operation had heart block necessitating pacemaker therapy. At postoperative electrophysiologic study, no echoes or atrioventricular nodal reentrant tachycardia were inducible in any of the 17 patients. All patients have remained free of arrhythmia recurrence and have required no antiarrhythmic therapy after a follow-up of 5 to 28 months (mean 14). In conclusion, both atrioventricular node skeletonization and perinodal cryosurgery successfully ablate atrioventricular nodal reentrant tachycardia; however, perinodal cryosurgery appears to be safer in avoiding heart block, is more easily performed, and is our procedure of choice for the management of medically refractory atrioventricular nodal reentrant tachycardia.