Semi automated segmentation of chromosomes in metaphase cells

Medical image segmentation plays a crucial role in many medical imaging applications by automating or facilating the delineation of anatomical structure and other regions of interest. This paper makes a review on the current segmentation algorithm used for retinal images. These algorithms are divided into three categories according to their main concepts like Edge-based, Region based and Special - theory based Segmentations. The reviewed algorithms are tested using Computer Vision and Image Processing (CVIP) tools.

Heuristic algorithms are effective methods for solving complex optimization problems. The optimal algorithm selection for a specific optimization problem is a challenging task. Fitness landscape analysis (FLA) is used to understand the optimization problem's characteristics and help select the optimal algorithm. A random walk algorithm is an essential technique for FLA in continuous search space. However, most currently proposed random walk algorithms suffer from unbalanced sampling points. This article proposes a Lévy flight-based random walk (LRW) algorithm to address this problem. The Lévy flight is used to generate the proposed random walk algorithm's variable step size and direction. Some tests show that the proposed LRW algorithm performs better in the uniformity of sampling points. Besides, the authors analyze the fitness landscape of the CEC2017 benchmark functions using the proposed LRW algorithm. The experimental results indicate that the proposed LRW algorithm can better obtain the structural features of the landscape and has better stability than several other RW algorithms.

Image segmentation plays a crucial role in many medical-imaging applications, by automating or facilitating the delineation of anatomical structures and other regions of interest. In this paper explaining current segmentation approaches in medical image segmentation and then reviewed with an emphasis on the advantages and disadvantages of these methods and showing the implemented outcomes of the thresholding, clustering,

Fenugreek or Trigonella foenum-graecum L. is a commercially important yet neglected crop of the family Fabaceae, with potent medicinal applications, and can treat several diseases as well. Conventional breeding studies for higher yields of commercial crops largely depend on chromosomal information of the particular species. Despite a number of cytological research being conducted on T. foenum-graecum, a complete characterization of its chromosomes has not been achieved due to the limitations of traditional karyotype analysis methods. A range of chromosomal markers are advantageous to characterize at full extent and identify individual chromosomes rather than relying on only physical metrics. Thus, in this study, in addition to giemsa staining, other approaches like fluorochrome and silver staining were used for the precise karyomorphological analysis of this species. Enzyme maceration and air drying (EMA) based fluorochrome banding with GC-specific stain Chromomycin A3 (CMA), and AT-specific stain 4’,6-diamidino-2-phenylindole (DAPI) applied for the first time for chromosome characterization. The results showed 2n = 16 chromosomes in metaphase cells, with karyotype formula of 2m+6sm. The unique banding pattern observed in the CMA/DAPI and AgNOR staining highlights the AT and GC-rich regions as well as the nucleolar organizer regions (NORs). All this crucial information can further assist in conducting breeding studies of more precision with simultaneously encouraging similar studies that need to be done in other unexploited species of importance.

In this paper, we introduce a new algorithm for estimating the lower bounds for the star discrepancy of any arbitrary point sets in [ 0 , 1 ] s [0,1]^{s} . Computing the exact star discrepancy is known to be an NP-hard problem, so we have been looking for effective approximation algorithms. The star discrepancy can be thought of as the maximum of a function called the local discrepancy, and we will develop approximation algorithms to maximize this function. Our algorithm is analogous to the random walk algorithm described in one of our previous papers [M. Alsolami and M. Mascagni, A random walk algorithm to estimate a lower bound of the star discrepancy, Monte Carlo Methods Appl. 28 (2022), 4, 341–348.]. We add a statistical technique to the random walk algorithm by implementing the Metropolis algorithm in random walks on each chosen dimension to accept or reject this movement. We call this Metropolis random walk algorithm. In comparison to all previously known techniques, our new algorithm is superior, especially in high dimensions. Also, it can quickly determine the precise value of the star discrepancy in most of our data sets of various sizes and dimensions, or at least the lower bounds of the star discrepancy.

The aim of this work is to present a modification of the Random Walker algorithm for the segmentation of occlusal caries from photographic color images. The modification improves the detection and time execution performance of the classical Random Walker algorithm and also deals with the limitations and difficulties that the specific type of images impose to the algorithm. The proposed modification consists of eight steps: 1) definition of the seed points, 2) conversion of the image to gray scale, 3) application of watershed transformation, 4) computation of the centroid of each region, 5) construction of the graph, 6) application of the Random Walker algorithm, 7) smoothing and extraction of the perimeter of the regions of interest and 8) overlay of the results. The algorithm was evaluated using a set of 96 images where 339 areas of interest were manually segmented by an expert. The obtained segmentation accuracy is 93%.

Diagnostic imaging is an invaluable tool in medicine. Different methods of getting images like Gamma Ray Imaging, X-Ray Imaging, Magnetic resonance imaging (MRI), Computed Tomography (CT), Digital Mammography, and other imaging modalities provide an effective means for noninvasive mapping the anatomy of a subject. These technologies have greatly increased knowledge of normal and diseased anatomy for medical research and are a critical component in diagnosis and treatment planning. With the limitation of number of exposures for Gamma Ray or X-Rays to the patient, it is difficult to see the critical feature if the intensity/brightness/ contrast are not proper. Moreover the growing size and number of these medical images have necessitated the use of computers to facilitate processing and analysis. In particular, computer algorithms for the intensity/ brightness/contrast adjustment, delineation of anatomical structures and other regions of interest are becoming increasingly important in assisting and automating specific radiological tasks.

Remote sensing classification is an important way to obtain land cover information, and the selection of classification training samples for most of the classification method is an expensive and time-consuming task. However, the traditional training samples selection method is a direct selection based on two-dimensional (2D) images, therefore, training sample selection efficiency is always low in the regions with complex terrain and landscape fragmentation, and the ROI (region of interest) separability is unsatisfactory for classification. This study aims at the low efficiency and low ROI separability for traditional training sample selection method put forward a new training sample selection method using a three-dimensional (3D) terrain model that was created by OLI image fusion digital elevation model (DEM) to select ROIs, which departs from the traditional method based on a two-dimensional image. A Landsat-8 OLI image of the Yunlong Reservoir Basin in Kunming was used to test this proposed method. Study results showed that the proposed method obtained ROI separability that was greater than 1.9, and with most reaching 2.0; while the ROI separability of traditional method still had unqualified situation, which showed the new method was more effective.

The modeling of the concrete matrix serves as a foundation for mesoscale analysis of concrete, which provides a crucial avenue for investigating the crack propagation and strength characteristics of concrete. However, the primary prerequisite for conducting such analyses is the generation of aggregate models. By combining the advantages of Voronoi diagrams and the random walk algorithm (RWA), a Voronoi–random walk algorithm is proposed in this paper. The algorithm overcomes the limitations of traditional methods, including constraints on aggregate volume fraction, low computational efficiency, and insufficient randomness in aggregate distribution. The meso-structure of a concrete block was modeled by the proposed method, and then its failure behavior under uniaxial compression was simulated using the finite element method. The numerical results agreed well with the experimental observations, indicating the effectiveness and accuracy of the proposed approach.

Automatic detection of region of interest (ROIs) in a complex image or video, such as angiogram pictures and neurosurgery video, is a critical task in many medical image and video processing applications. In this paper, we present a new method that addresses several challenges in automatic detection of ROI of neurosurgical video for ROI coding which is used for neurophysiological intraoperative monitoring (IOM) system. This method is based on an object tracking technique with the multivariate density estimation theory, combined with the shape information of the object in the neurosurgical video. By defining the ROIs for neurosurgical video, this method produces a smooth and convex emphasis region within which surgical procedures are performed. A large bandwidth budget is assigned within the ROI to archive high-fidelity Internet transmission. Outside the ROI, a small bandwidth budget is allocated to efficiently utilize the bandwidth resource in Internet connection. And we believe this method also can be used to the image-guide surgery (IGS) system to track the positions of surgical instruments in the physical space occupied by the patient after some improvement.

Objective. The purpose of this study was to evaluate the length of telomeres in the arms of individual chromosomes in patients with bronchial asthma (BA).Materials and methods. The study included patients with BA (n = 10, the mean age (44 ± 8.2) years) and healthy donors (n = 10, the mean age (44 ± 8.4) years). Metaphase spreads obtained from peripheral blood mononuclear cells were used. At the time of sampling BA patients received treatment at the Clinic of Immunopathology, Novosibirsk. BA was diagnosed by physicians according to GINA-2016. For measurement of telomere length on individual chromosome arms we used quantitative fluorescent in situ hybridization with a PNA-probe specific for telomeres. We used inverted DAPI banding for chromosome identification (according to ISCN-2013). For each individual 5 metaphase cells were analyzed. We applied the newly developed MeTeLen software to estimate the telomere repeats quantity (http:// www.bionet.nsc.ru/en/development/application-development/development-of-a-computer/metelen.html) in metaphase images. For enhanced image analysis compared with the previously developed programs, we included estimation of background signal and correction of defects of the optical system.Results. Comparing of telomere length show, that telomeres in the certain chromosome arms (4q, 5q, 9p, 10 q, 11p, 13p, 15q, 18q, 19q) in BA are significantly shorter than in corresponding group of donors (p < 0.05, Mann – Whitney U-test). For both studied groups we also evaluated telomere sequences shortened and elongated relative to the average telomere length in the group (p < 0.05, Wilcoxon-signed-runk test). The following differences and similarities between the telomere profiles of patients and donors were determined: the telomere sequences 4p, 6q, 8p were elongated and 2q, 9q, 11p, 15q were shortened relative to the average telomere length in BA patients. Moreover, this telomere sequences did not differ from the average telomere length in the group of donors. At the same time, the telomere sequences 12p, 16p, 17p, 19p were significantly shorter, and 3p was longer than the average telomere length in both groups.Conclusions. We guess, that the observed significant shortening of telomere length on individual chromosome arms in BA, as compared to donors, is relevant in pathogenesis of this disorder. The revealed features of telomere profile of patients with BA may be a result of different telomere length maintenance mechanisms and may influence to the development of asthma that needs further study.

Automatic detection of region of interest (ROIs) in a complex image or video, such as an angiogram or endoscopic neurosurgery video, is a critical task in many medical image and video processing applications. In this paper, we present a new method that addresses several challenges in automatic detection of ROI of neurosurgical video for ROI coding which is used for neurophysiological intraoperative monitoring (IOM) system. This method is based on an object tracking technique with multivariate density estimation theory, combined with the shape information of the object. By defining the ROIs for neurosurgical video, this method produces a smooth and convex emphasis region within which surgical procedures are performed. A large bandwidth budget is assigned within the ROI to archive high-fidelity Internet transmission. Outside the ROI, a small bandwidth budget is allocated to efficiently utilize the bandwidth resource. We believe this method also can be used to image-guiduance surgery (IGS) systems to track the positions of surgical instruments in the physical space occupied by the patient after some improvement.

We analyze the efficiency of the random walk algorithm on random 3-CNF instances, and prove linear upper bounds on the running time of this algorithm for small clause density, less than 1.63. Our upper bound matches the observed running time to within a multiplicative factor. This is the first sub-exponential upper bound on the running time of a local improvement algorithm on random instances. Our proof introduces a simple, yet powerful tool for analyzing such algorithms, which may be of further use. This object, called a terminator, is a weighted satisfying assignment. We show that any CNF having a good (small weight) terminator is assured to be solved quickly by the random walk algorithm. This raises the natural question of the terminator threshold which is the maximal clause density for which such assignments exist (with high probability). We use the analysis of the pure literal heuristic presented by Broder, Frieze and Upfal and show that for small clause densities good terminators exist. Thus we show that the pure literal threshold (/spl ap/ 1.63) is a lower bound on the terminator threshold. One nice property of terminators is that they can be found efficiently, via linear programming. This makes tractable the future investigation of the terminator threshold, and also provides an efficiently computable certificate for short running time of the simple random-walk heuristic.

The segmentation of tumor on PET images is an important step for treatment planning process during the radiotherapy. In this paper, we present an automatic segmentation method on PET images based on the random walks (RW) algorithm. We propose an extension of the random walks framework to integrate a tumor evolution information, which is the predicted tumor region resulting from a model for lung tumor growth and response to radiotherapy. The region of interest (ROI) and labeled seeds are automatically generated. Our approach is compared to the well-known 40% thresholding method, an adaptive thresholding method, a statistical method (FLAB), and a traditional RW algorithm. The good performance of our method has been confirmed on 7 lung tumor patients who are treated with radiotherapy.

Breast cancer is one of the most common cancer types especially met in women. The number of breast cancer patients increases every year. Thus, to detect breast cancer at its early stages gains importance. Breast region detection is the pioneering step of breast cancer diagnosis researches performed via image processing techniques. The performance of computer-aided breast cancer diagnosis systems can be improved by exactly determining the breast region of interest. In this study, the goal is to determine a region of interest for breast MR images, in which one or more lesion can appear. The achieved region includes two breasts and lymph nodes. The proposed region of interest detection system is fully automatic and it utilizes several image processing techniques. At first, the local adaptive thresholding technique is applied to the noise-filtered grey level breast magnetic resonance images taken with ethical permissions from Sakarya Education and Research Hospital. After adaptive thresholding, connected component analysis is performed to exclude extra structures around the breast region as thorax area. This analysis selects the largest area in the binary image which corresponds to a gyrate region including breast area and lymph nodes over the backbone. Then, the integral of horizontal projection is calculated to determine an optimum horizontal line that allows setting the region of interest apart. In the following step, sternum midpoint is detected to separate the right breast from the left one. Finally, a masking operation is applied to get corresponding right and left breast regions in the original MR image. To evaluate the performance of the proposed study, the results of automatic region of interest detection system are compared with the manual region of interest selection performed by an expert radiologist. Dice similarity coefficient and Jaccard coefficient are used as performance criteria. According to the results, the proposed system can detect region of interest for computer-aided breast cancer diagnosis researches, exactly.