Sliding Band Filter Research Articles

Automatic Classification of Hypertensive Retinopathy by Gray Wolf Optimization Algorithm and Naïve Bayes Classification

Retinal blood vessels are affected by a variety of eye diseases, including hypertensive retinopathy (HR) and diabetic retinopathy (DR). A person with HR needs to be sure to check their eyes regularly, which requires the use of computer vision methods to analyze images of the back of the eye and help ophthalmologists automatically. Automated diagnostic systems are useful for diagnosing different retinal diseases for ophthalmologists and patients who need to establish an automated HR detection and classification system using retinal images. In this work, a sliding band filter was used to improve the back-of-the-eye images and small convex regions to develop an automated system for detecting and classifying HR gravity levels. An image classification with improved wolf optimization along Bayes algorithm was conducted. The current model was tested using the publicly available dataset, and its results were compared to existing models. The results mentioned that the model-improved Naïve Bayes model classified the different HR severity levels on the optimized features and produced a maximum accuracy of 100% while being compared to other classifiers.

Microaneurysm detection in color eye fundus images for diabetic retinopathy screening

Diabetic retinopathy (DR) is a diabetes complication, which in extreme situations may lead to blindness. Since the first stages are often asymptomatic, regular eye examinations are required for an early diagnosis. As microaneurysms (MAs) are one of the first signs of DR, several automated methods have been proposed for their detection in order to reduce the ophthalmologists’ workload. Although local convergence filters (LCFs) have already been applied for feature extraction, their potential as MA enhancement operators was not explored yet. In this work, we propose a sliding band filter for MA enhancement aiming at obtaining a set of initial MA candidates. Then, a combination of the filter responses with color, contrast and shape information is used by an ensemble of classifiers for final candidate classification. Finally, for each eye fundus image, a score is computed from the confidence values assigned to the MAs detected in the image. The performance of the proposed methodology was evaluated in four datasets. At the lesion level, sensitivities of 64% and 81% were achieved for an average of 8 false positives per image (FPIs) in e-ophtha MA and SCREEN-DR, respectively. In the last dataset, an AUC of 0.83 was also obtained for DR detection.

Conventional Filtering Versus U-Net Based Models for Pulmonary Nodule Segmentation in CT Images.

Lung cancer is considered one of the deadliest diseases in the world. An early and accurate diagnosis aims to promote the detection and characterization of pulmonary nodules, which is of vital importance to increase the patients' survival rates. The mentioned characterization is done through a segmentation process, facing several challenges due to the diversity in nodular shape, size, and texture, as well as the presence of adjacent structures. This paper tackles pulmonary nodule segmentation in computed tomography scans proposing three distinct methodologies. First, a conventional approach which applies the Sliding Band Filter (SBF) to estimate the filter's support points, matching the border coordinates. The remaining approaches are Deep Learning based, using the U-Net and a novel network called SegU-Net to achieve the same goal. Their performance is compared, as this work aims to identify the most promising tool to improve nodule characterization. All methodologies used 2653 nodules from the LIDC database, achieving a Dice score of 0.663, 0.830, and 0.823 for the SBF, U-Net and SegU-Net respectively. This way, the U-Net based models yield more identical results to the ground truth reference annotated by specialists, thus being a more reliable approach for the proposed exercise. The novel network revealed similar scores to the U-Net, while at the same time reducing computational cost and improving memory efficiency. Consequently, such study may contribute to the possible implementation of this model in a decision support system, assisting the physicians in establishing a reliable diagnosis of lung pathologies based on this segmentation task.

Towards the automatic segmentation of HEp-2 cells in indirect immunofluorescence images using an efficient filtering based approach

The computer aided analysis of Indirect-Immunofluorescence (IIF) images is important for the differential diagnosis of several autoimmune diseases. A fully automatic approach consists in segmentation of individual cells in IIF images and subsequently its classification into various pattern types. This paper explores the segmentation of HEp2 cell in IIF images through the use of a filtering based approach. Our algorithm is based on a local convergence filter named as Sliding Band Filter (SBF). We propose a modified SBF that is capable of handling the low contrast, noise and illumination variations peculiar to IIF images. In addition, we follow a simple algorithmic pipeline and achieve better accuracy as compared to several state of the art segmentation algorithms on standard HEp2 image dataset.

Detection and Delineation of Sorted Stone Circles in Antarctica

Sorted stone circles are natural surface patterns formed in periglacial environments. Their relation to permafrost conditions make them very helpful for better understanding the past climates where they were formed and have evolved and also for monitoring current underlying processes in case circles are active. These metric scale patterns that occur in clusters of tens to thousands of circular elements, can be more comprehensively characterized if automated methods are used. This paper addresses their identification and delineation through the development and testing of a set of automated approaches, namely, template matching, sliding band filter, and dynamic programming. All of these methods take advantage of the 3D shape of the structures conveyed by digital elevation models (DEM), built from ultra-high resolution imagery captured by unmanned aerial vehicles (UAV) surveys developed in Barton Peninsula, King George Island, Antarctica (62°S). The best detection results achieve scores above 85%, while the delineations are performed with errors as low as 7%.

Neuron anatomy structure reconstruction based on a sliding filter.

BackgroundReconstruction of neuron anatomy structure is a challenging and important task in neuroscience. However, few algorithms can automatically reconstruct the full structure well without manual assistance, making it essential to develop new methods for this task.MethodsThis paper introduces a new pipeline for reconstructing neuron anatomy structure from 3-D microscopy image stacks. This pipeline is initialized with a set of seeds that were detected by our proposed Sliding Volume Filter (SVF), given a non-circular cross-section of a neuron cell. Then, an improved open curve snake model combined with a SVF external force is applied to trace the full skeleton of the neuron cell. A radius estimation method based on a 2D sliding band filter is developed to fit the real edge of the cross-section of the neuron cell. Finally, a surface reconstruction method based on non-parallel curve networks is used to generate the neuron cell surface to finish this pipeline.ResultsThe proposed pipeline has been evaluated using publicly available datasets. The results show that the proposed method achieves promising results in some datasets from the DIgital reconstruction of Axonal and DEndritic Morphology (DIADEM) challenge and new BigNeuron project.ConclusionThe new pipeline works well in neuron tracing and reconstruction. It can achieve higher efficiency, stability and robustness in neuron skeleton tracing. Furthermore, the proposed radius estimation method and applied surface reconstruction method can obtain more accurate neuron anatomy structures.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0780-0) contains supplementary material, which is available to authorized users.

Realce de candidatos a nódulo pulmonar en radiografías de tórax por medio de filtros de convergencia

Este artículo presenta el desarrollo de una herramienta de software para el realce de áreas sospechosas de nódulos pulmonares en radiografías de tórax. Los nódulos pulmonares son lesiones que se encuentran dentro de la regíon pulmonar y son los primeros indicadores de la presencia de un tumor cancerígeno en este órgano. En primera instancia, se implementa un método de segmentación automático. Luego, para el realce de los candidatos, se usan filtros de convergencia que evalúan el grado en el que converge el área circundante al píxel de interés, asumiendo que los posibles candidatos a nódulos presentan forma circular. En este trabajo se implementan cuatro filtros de convergencia: Moneda, Iris, Anillo Adaptativo y Banda Deslizante. Los filtros de moneda y de anillo adaptativo obtuvieron mejores resultados en cuanto a detección y número de candidatos por imagen. Al aplicar la técnica de resta contralateral junto con los filtros basados en características geométricas tales como área, redondez y excentricidad, se logró disminuir el número de candidatos.

Optic disc segmentation using the sliding band filter

Background: The optic disc (OD) centre and boundary are important landmarks in retinal images and are essential for automating the calculation of health biomarkers related with some prevalent systemic disorders, such as diabetes, hypertension, cerebrovascular and cardiovascular diseases.Methods: This paper presents an automatic approach for OD segmentation using a multiresolution sliding band filter (SBF). After the preprocessing phase, a low-resolution SBF is applied on a downsampled retinal image and the locations of maximal filter response are used for focusing the analysis on a reduced region of interest (ROI). A high-resolution SBF is applied to obtain a set of pixels associated with the maximum response of the SBF, giving a coarse estimation of the OD boundary, which is regularized using a smoothing algorithm.Results: Our results are compared with manually extracted boundaries from public databases (ONHSD, MESSIDOR and INSPIRE-AVR datasets) outperforming recent approaches for OD segmentation. For the ONHSD, 44% of the results are classified as Excellent, while the remaining images are distributed between the Good (47%) and Fair (9%) categories. An average overlapping area of 83%, 89% and 85% is achieved for the images in ONHSD, MESSIDOR and INSPIR-AVR datasets, respectively, when comparing with the manually delineated OD regions.Discussion: The evaluation results on the images of three datasets demonstrate the better performance of the proposed method compared to recently published OD segmentation approaches and prove the independence of this method when from changes in image characteristics such as size, quality and camera field of view.

Bright field microscopic cells counting method for BEVS using nonlinear convergence index sliding band filter

BackgroundThe Baculovirus Expression Vector System (BEVS) is a very popular expression vector system in gene engineering. An effective host cell line cultivation protocol can facilitate the baculovirus preparation and following experiments. However, the counting of the number of host cells in the protocol is usually performed by manual observation with microscopy, which is time consuming and labor intensive work, and prone to errors for one person or between different individuals. This study aims at giving a bright field insect cells counting protocol to help improve the efficient of BEVS.MethodTo develop a reliable and accurate counting method for the host cells in the bright field, such as Sf9 insect cells, a novel method based on a nonlinear Transformed Sliding Band Filter (TSBF) was proposed. And 3 collaborators counted cells at the same time to produce the ground truth for evaluation. The performance of TSBF method was evaluated with the image datasets of Sf9 insect cells according to the different periods of cell cultivation on the cell density, error rate and growth curve.ResultsThe average error rate of our TSBF method is 2.21% on average, ranging from 0.89% to 3.97%, which exhibited an excellent performance with its high accuracy in lower error rate compared with traditional methods and manual counting. And the growth curve was much the manual method well.ConclusionResults suggest the proposed TSBF method can detect insect cells with low error rate, and it is suitable for the counting task in BEVS to take the place of manual counting by humans. Growth curve results can reflect the cells’ growth manner, which was generated by our proposed TSBF method in this paper can reflected the similar manner with it’s from the manual method. All of these proven that the proposed insect cell counting method can clearly improve the efficiency of BEVS.

A counting method for density packed cells based on sliding band filter image enhancement

Cell loss and addition is an important biological event in pathology, and it usually provides central information to the changes of biological activity in the histological sections. To develop a reliable and accurate cell counting tools in tissue section, in this paper, we proposed a novel cell nuclei detecting method based on the sliding band filter which is a member of convergence index family. We evaluated the accuracy and performance of our method on density packed retinal outer nuclear layer cell confocal multivariate fluorescence microscopy image datasets. The results show our proposed method exhibited an excellent performance with its accuracy compared with human manual counting. It is worth noting that the proposed cell counting method can clearly benefit for retinal detachment and reattachment visual diagnostics close related to cell loss and addition.

Cell Nuclei and Cytoplasm Joint Segmentation Using the Sliding Band Filter

Microscopy cell image analysis is a fundamental tool for biological research. In particular, multivariate fluorescence microscopy is used to observe different aspects of cells in cultures. It is still common practice to perform analysis tasks by visual inspection of individual cells which is time consuming, exhausting and prone to induce subjective bias. This makes automatic cell image analysis essential for large scale, objective studies of cell cultures. Traditionally the task of automatic cell analysis is approached through the use of image segmentation methods for extraction of cells' locations and shapes. Image segmentation, although fundamental, is neither an easy task in computer vision nor is it robust to image quality changes. This makes image segmentation for cell detection semi-automated requiring frequent tuning of parameters. We introduce a new approach for cell detection and shape estimation in multivariate images based on the sliding band filter (SBF). This filter's design makes it adequate to detect overall convex shapes and as such it performs well for cell detection. Furthermore, the parameters involved are intuitive as they are directly related to the expected cell size. Using the SBF filter we detect cells' nucleus and cytoplasm location and shapes. Based on the assumption that each cell has the same approximate shape center in both nuclei and cytoplasm fluorescence channels, we guide cytoplasm shape estimation by the nuclear detections improving performance and reducing errors. Then we validate cell detection by gathering evidence from nuclei and cytoplasm channels. Additionally, we include overlap correction and shape regularization steps which further improve the estimated cell shapes. The approach is evaluated using two datasets with different types of data: a 20 images benchmark set of simulated cell culture images, containing 1000 simulated cells; a 16 images Drosophila melanogaster Kc167 dataset containing 1255 cells, stained for DNA and actin. Both image datasets present a difficult problem due to the high variability of cell shapes and frequent cluster overlap between cells. On the Drosophila dataset our approach achieved a precision/recall of 95%/69% and 82%/90% for nuclei and cytoplasm detection respectively and an overall accuracy of 76%.