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EN
Medical imaging tasks, such as segmentation, 3D modeling, and registration of medical images, involve complex geometric problems, usually solved by standard linear algebra and matrix calculations. In the last few decades, conformal geometric algebra (CGA) has emerged as a new approach to geometric computing that offers a simple and efficient representation of geometric objects and transformations. However, the practical use of CGA-based methods for big data image processing in medical imaging requires fast and efficient implementations of CGA operations to meet both real-time processing constraints and accuracy requirements. The purpose of this study is to present a novel implementation of CGA-based medical imaging techniques that makes them effective and practically usable. The paper exploits a new simplified formulation of CGA operators that allows significantly reduced execution times while maintaining the needed result precision. We have exploited this novel CGA formulation to re-design a suite of medical imaging automatic methods, including image segmentation, 3D reconstruction and registration. Experimental tests show that the re-formulated CGA-based methods lead to both higher precision results and reduced computation times, which makes them suitable for big data image processing applications. The segmentation algorithm provides the Dice index, sensitivity and specificity values of 98.14%, 98.05% and 97.73%, respectively, while the order of magnitude of the errors measured for the registration methods is 10-5.
EN
A magnetic anomaly map of an underwater area indicates the places where the distortion of a magnetic field has occurred. Through the interpretation procedures, a hydrographer can easily indicate the places where the ferromagnetic objects are, then calculate the level of each distortion – by the value of total anomaly – and initially, based on their own knowledge, try to classify the sources of distortion. Objects that induce micro anomaly changes (>30 nT) – like industrial infrastructure, such as pipelines and cables; to unintendingly located targets with ferromagnetic characteristics: wrecks (vessels, planes, cars), military mines, UXO, lost anchors and chains. Interpretation of such a map with the attempt to identify the source of magnetic field distortion, requires a specific knowledge as well as experience. In this article the author presents the research results of dimensioning and location of potential ferromagnetic underwater objects based on a magnetic anomaly map. For further consideration an anchor of buoyage system is taken into account. Geolocation of ferromagnetic sources, contours extraction and dimensioning algorithms of ferromagnetic targets have been carried out in Matlab software. The map of magnetic anomaly enhanced with extracted information was developed in ArcGIS. The analysis was carried out for the purpose of the dissertation thesis and the results are used in further research.
PL
Mapa anomalii magnetycznych obszaru podwodnego wskazuje miejsca, w których występuje zniekształcenie ziemskiego pola magnetycznego. Za pomocą procedur interpretacyjnych hydrograf może łatwo wskazać miejsca, w których znajdują się obiekty ferromagnetyczne, a następnie obliczyć poziom każdego zniekształcenia – według wartości całkowitej anomalii – i na podstawie własnej wiedzy spróbować sklasyfikować źródła zniekształceń. Obiekty, które indukują zniekształcenie pola magnetycznego na obszarach wodnych, mogą być różne. Te wywołujące zmiany pola magnetycznego (anomalia >30 nT) to między innymi infrastruktura przemysłowa, np.: rurociągi i kable, a także nieumyślnie zlokalizowane cele o charakterystyce ferromagnetycznej: wraki (statków, samolotów, samochodów), miny wojskowe, niewybuchy, kotwice i łańcuchy statków. Interpretacja takiej mapy w celu zidentyfikowania źródła zniekształcenia pola magnetycznego wymaga specjalistycznej wiedzy i doświadczenia. Całkowita wartość anomalii magnetycznej określa wielkość poziomu ferromagnetyzmu obiektu, a wymiar powierzchni objętej anomalią umożliwia geolokalizację celu i ustalenie jego wymiarów. W artykule autorzy przedstawiają wyniki badań wymiarowania i lokalizacji potencjalnych ferromagnetycznych podwodnych obiektów na podstawie mapy anomalii magnetycznych. Przeanalizowano anomalię magnetyczną spowodowaną przez kotwicę oznakowania nawigacyjnego. Geolokalizacja źródeł ferromagnetycznych, ekstrakcja ich konturów i algorytmy wymiarowania celów ferromagnetycznych zostały przeprowadzone za pomocą oprogramowania Matlab. Porównano i podsumowano wyniki działania różnych filtrów stosowanych do przetwarzania obrazów. Mapa anomalii magnetycznej wzbogacona o wyodrębnione informacje została opracowana w ArcGIS. Analiza została przeprowadzona na potrzeby pracy doktorskiej, a jej wyniki wykorzystano w dalszych badaniach
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Content available remote Speckle noise reduction and image segmentation based on a modified mean filter
EN
Image segmentation is an essential process in many fields involving digital images. In gen-eral, segmentation is the process of dividing the image into objects and background image.Image segmentation is an important step in the object detection process. It becomes morecritical if a given image is corrupted by noise. Most digital images are corrupted by noisessuch as salt and pepper noise, Gaussian noise, Poisson noise, speckle noise, etc. Specklenoise is a multiplicative noise that affects pixels in a gray-scale image, and mainly occursin low level luminance images such as Synthetic Aperture Radar (SAR) images and Mag-netic Resonance Image (MRI) images. Image enhancement is an essential task to reducespecklenoise prior to performing further image processing such as object detection, imagesegmentation, edge detection, etc. Here, we propose a neighborhood-based algorithm toreduce speckle noise in gray-scale images. The main aim of the noise reduction technique isto segment the noisy image. So that the proposed algorithm applies some luminance to theoriginal image. The proposed technique performs well at maximum noise variance. Finally,the segmentation process is done by the modified mean filter. The proposed technique hasthree phases. In phase 1, the speckle noise is reduced and the contrast adjustment is made.In phase 2, the segmentation of the enhanced image is processed. Finally, in phase 3, theisolated pixels in the segmented image are eliminated and the final segmented image isgenerated. This technique does not require any threshold value to segment the image; itwill be automatically calculated based on the mean value.
EN
Segmentation of retinal layers is a vital and important step in computerized processing and the study of retinal Optical Coherence Tomography (OCT) images. However, automatic segmentation of retinal layers is challenging due to the presence of noise, widely varying reflectivity of image components, variations in morphology and alignment of layers in the presence of retinal diseases. In this paper, we propose a Fully Convolutional Network (FCN) termed as DelNet based on a deep ensemble learning approach to selectively segment retinal layers from OCT scans. The proposed model is tested on a publicly available DUKE DME dataset. Comparative analysis with other state-of-the-art methods on a benchmark dataset shows that the performance of DelNet is superior to other methods.
5
EN
Accurate segmentation of brain tissues in magnetic resonance imaging (MRI) data plays critical role in the clinical diagnostic and treatment planning. The presence of noise and artifacts in MRI data degrades the performance of segmentation algorithms. In this view, the present study proposes a complete unsupervised clustering based multi-objective modified fuzzy c-mean (MOFCM) segmentation algorithm, which inculcates multi-objective antlion optimization (MOALO) to minimize the cluster compactness and fuzzy hyper-volume fitness functions. The output segmented image corresponds to minimum value of partition entropy in the obtained solution set. The present study integrates proposed MOFCM with a new cluster number validity index, which allows user not to provide number of segments in image as an input. The proposed MOFCM algorithm is extensively validated on seventy two synthetic images corrupted with different levels of Gaussian, Speckle and Rician noises, forty simulated BrainWeb MRI images suffered from noise and inhomogeneity, and 10 real IBSR MRI dataset of images. The results are compared with existing popular clustering based algorithms, and supervised deep learning based algorithms, i.e. UNet, SegNet and Quick- NAT. The proposed MOFCM algorithm demonstrate the superior segmentation performance in comparison to popular FCM based clustering algorithms, SegNet and UNet, whereas the segmentation results of proposed MOFCM are at par with QuickNAT.
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Content available remote Multi-path convolutional neural network in fundus segmentation of blood vessels
EN
There is a close correlation between retinal vascular status and physical diseases such as eye lesions. Retinal fundus images are an important basis for diagnosing diseases such as diabetes, glaucoma, hypertension, coronary heart disease, etc. Because the thickness of the retinal blood vessels is different, the minimum diameter is only one or two pixels wide, so obtaining accurate measurement results becomes critical and challenging. In this paper, we propose a new method of retinal blood vessel segmentation that is based on a multi-path convolutional neural network, which can be used for computer-based clinical medical image analysis. First, a low-frequency image characterizing the overall characteristics of the retinal blood vessel image and a high-frequency image characterizing the local detailed features are respectively obtained by using a Gaussian low-pass filter and a Gaussian high-pass filter. Then a feature extraction path is constructed for the characteristics of the low- and high-frequency images, respectively. Finally, according to the response results of the low-frequency feature extraction path and the high-frequency feature extraction path, the whole blood vessel perception and local feature information fusion coding are realized, and the final blood vessel segmentation map is obtained. The performance of this method is evaluated and tested by DRIVE and CHASE_DB1. In the experimental results of the DRIVE database, the evaluation indexes accuracy (Acc), sensitivity (SE), and specificity (SP) are 0.9580, 0.8639, and 0.9665, respectively, and the evaluation indexes Acc, SE, and SP of the CHASE_DB1 database are 0.9601, 0.8778, and 0.9680, respectively. In addition, the method proposed in this paper could effectively suppress noise, ensure continuity after blood vessel segmentation, and provide a feasible new idea for intelligent visual perception of medical images.
EN
For the Convolutional Neural Networks (CNNs) applied in the intelligent diagnosis of gastric cancer, existing methods mostly focus on individual characteristics or network frameworks without a policy to depict the integral information. Mainly, conditional random field (CRF), an efficient and stable algorithm for analyzing images containing complicated contents, can characterize spatial relation in images. In this paper, a novel hierarchical conditional random field (HCRF) based gastric histopathology image segmentation (GHIS) method is proposed, which can automatically localize abnormal (cancer) regions in gastric histopathology images obtained by an optical microscope to assist histopathologists in medical work. This HCRF model is built up with higher order potentials, including pixel-level and patch-level potentials, and graph-based post-processing is applied to further improve its segmentation performance. Especially, a CNN is trained to build up the pixel-level potentials and another three CNNs are fine-tuned to build up the patch-level potentials for sufficient spatial segmentation information. In the experiment, a hematoxylin and eosin (H&E) stained gastric histopathological dataset with 560 abnormal images are divided into training, validation and test sets with a ratio of 1 : 1 :2. Finally, segmentation accuracy, recall and specificity of 78.91%, 65.59%, and 81.33% are achieved on the test set. Our HCRF model demonstrates high segmentation performance and shows its effectiveness and future potential in the GHIS field.
EN
Leukemia is an abnormal proliferation of leukocytes in the bone marrow and blood and it is usually diagnosed by the pathologists by observing the blood smear under a microscope. The count of various cells and their morphological features are used by the pathologists to identify and classify leukemia. An abnormal increase in the count of immature leukocytes along with a reduced count of other blood cells may be an indication of leukemia. The Pathologist may then recommend for bone marrow examination to confirm and identify the specific type of leukemia. These conventional methods are time consuming and may be affected by the skill and expertise of the medical professionals involved in the diagnostic procedures. Image processing based methods can be used to analyze the microscopic smear images to detect the incidence of leukemia automatically and quickly. Image segmentation is one of the very important tasks in processing and analyzing medical images. In the proposed paper an attempt has been made to review the available works in the area of medical image processing of blood smear images, highlighting automated detection of leukemia. The available works in the related area are reviewed based on the segmentation method used. It is learnt that even though there are many studies for detection of acute leukemia only a very few studies are there for the detection of chronic leukemia. There are a few related review studies available in the literature but, none of the works classify the previous studies based on the segmentation method used.
EN
The aim of the study was to create an accurate method of automated subcutaneous (SAT) and visceral (VAT) adipose tissue detection basing on three-dimensional (3D) computed tomography (CT) scans. One hundred and forty abdominal CT examinations were analysed. An algorithm for automated detection of SAT and VAT consisted of following steps: thresholding of an analysed image, detection of a patient's body region, separation of SAT and VAT. The algorithm was sequentially applied to each 2D axial slice of a 3D examination. To assess the accuracy of the proposed method, automated and manual segmentations (performed by two readers) of SAT and VAT were compared using Dice similarity coefficient (DSC) and average Hausdorff distance (AHD). Mean DSC was equal to 99.6% ± 0.4% for SAT and 99.6% ± 0.5% for VAT, which was equal to DSC obtained for comparison between both readers. In 90% of cases DSC was equal or above 99.0% and the minimal DSC was 97.6%. AHD equalled to 0.04 ± 0.06 for SAT and 0.13 ± 0.23 for VAT (automated vs. manual segmentations), while AHD for comparison of two manual segmentations was 0.03 ± 0.07 for SAT and 0.09 ± 0.20 for VAT. The processing time for a single slice was 0.16 s for an automated segmentation and 510 min for a manual segmen- tation. The processing time of an entire 3D stack (around 40 2D slices) was on average 6.5 s. Our algorithm for the automated detection of SAT and VAT on 3D CT scans has the same accuracy as manual segmentation and performs equally well for both adipose tissue compartments.
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Content available remote Review of Printed Fabric Pattern Segmentation Analysis and Application
EN
Image processing of digital images is one of the essential categories of image transformation in the theory and practice of digital pattern analysis and computer vision. Automated pattern recognition systems are much needed in the textile industry more importantly when the quality control of products is a significant problem. The printed fabric pattern segmentation procedure is carried out since human interaction proves to be unsatisfactory and costly. Hence, to reduce the cost and wastage of time, automatic segmentation and pattern recognition are required. Several robust and efficient segmentation algorithms are established for pattern recognition. In this paper, different automated methods are presented to segregate printed patterns from textiles fabric. This has become necessary because quality product devoid of any disturbances is the ultimate aim of the textile printing industry.
PL
Stworzenie nowej metody estymacji map głębi przeznaczonej dla systemów telewizji swobodnego widzenia jest głównym celem przedstawionych badań. W telewizji swobodnego punktu widzenia możliwości widza są rozszerzone poprzez możliwość kontroli aktualnie oglądanego przez niego punktu widzenia sceny. Nowa metoda estymacji map głębi zaproponowana przez autora składa się z trzech części: przestrzennie spójnej estymacji map głębi opartej na segmentacji widoków, metodzie zwiększenia spójności czasowej map głębi zmniejszającej złożoność obliczeniową estymacji oraz nową metodę zrównoleglania procesu optymalizacji opartego na wykorzystaniu grafów.
EN
The development of a novel depth estimation method for free viewpoint television systems is the main goal of presented research. In free viewpoint television the functionalities offered to a viewer are extended by the possibility of controlling the displayed viewpoint of a scene. The novel method for depth estimation proposed by the author of the dissertation consists of three parts: the interview consistent segment-based depth estimation method, the temporal consistency enhancement that simultaneously increases temporal consistency of depth maps and reduces the complexity of estimation, and a new method of parallelisation for graph based depth estimation methods.
12
Content available remote Methods of picture segmentation in recognition digital satellite images
EN
In the article for the recognition of digital satellite images, the method of segmentation of views by thresholding was chosen. Two algorithms were used: Laplasian of Gaussian and Canny. The Laplasian of Gaussian algorithm with Gauss low-pass filter smoothes the edges and Laplace's high-pass filter sharpens the image. Based on the calculations made, clear boundaries between individual areas were obtained. The presented application in the MATLAB environment effectively detects forest areas and lakes in the satellite images.
PL
W artykule do rozpoznawania cyfrowych zdjęć satelitarnych wybrano metodę segmentacji zobrazowań przez progowanie. Zastosowano dwa algorytmy: Laplasian of Gaussian i Canny’ego. Algorytm Laplasian of Gaussian z filtrem dolnoprzepustowym Gaussa wygładza krawędzie a filtr górnoprzepustowy Laplace’a wyostrza obraz. Na podstawie przeprowadzonych obliczeń otrzymano wyraźne granice między poszczególnymi obszarami. Przedstawiona aplikacja w środowisku MATLAB skutecznie wykrywa obszary leśne i jeziora na zdjęciach satelitarnych.
EN
In the present work, the performance assessment of despeckle filtering algorithms has been carried out for (α) noise reduction in breast ultrasound images and (b) segmentation of benign and malignant tumours from breast ultrasound images. The despeckle filtering algorithms are broadly classified into eight categories namely local statistics based filters, fuzzy filters, Fourier filters, multiscale filters, non-linear iterative filters, total variation filters, non-local mean filters and hybrid filters. Total 100 breast ultrasound images (40 benign and 60 malignant) are processed using 42 despeckle filtering algorithms. A despeckling filter is considered to be appropriate if it preserves edges and features/structures of the image. Edge preservation capability of a despeckling filter is measured by beta metric (β) and feature/structure preservation capability is quantified using image quality index (IQI). It is observed that out of 42 filters, six filters namely Lee Sigma, FI, FB, HFB, BayesShrink and DPAD yield more clinically acceptable images in terms of edge and feature/structure preservation. The qualitative assessment of these images has been done on the basis of grades provided by the experienced participating radiologist. The pre-processed images are then fed to a segmentation module for segmenting the benign or malignant tumours from ultrasound images. The performance assessment of segmentation algorithm has been done quantitatively using the Jaccard index. The results of both quantitative and qualitative assessment by the radiologist indicate that the DPAD despeckle filtering algorithm yields more clinically acceptable images and results in better segmentation of benign and malignant tumours from breast ultrasound images.
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Content available remote A hybrid method for blood vessel segmentation in images
EN
In the last years, image processing has been an important tool for health care. The analysis of retinal vessel images has become crucial to achieving a better diagnosis and treatment for several cardiovascular and ophthalmological deceases. Therefore, an automatic and accurate procedure for retinal vessel and optic disc segmentation is essential for illness detection. This task is extremely hard and time-consuming, often requiring the assistance of human experts with a high degree of professional skills. Several retinal vessel segmentation methods have been developed with satisfactory results. Nevertheless, most of such techniques present a poor performance mainly due to the complex structure of vessels in retinal images. In this paper, an accurate methodology for retinal vessel and optic disc segmentation is presented. The proposed scheme combines two different techniques: the Lateral Inhibition (LI) and the Differential Evolution (DE). The LI scheme produces a new image with enhanced contrast between the background and retinal vessels. Then, the DE algorithm is used to obtain the appropriate threshold values through the minimization of the cross-entropy function from the enhanced image. To evaluate the performance of the proposed approach, several experiments over images extracted from STARE, DRIVE, and DRISHTI-GS databases have been conducted. Simulation results demonstrate a high performance of the proposed scheme in comparison with similar methods reported in the literature.
EN
Over the last few years, deep learning has proven to be a great solution to many problems, such as image or text classification. Recently, deep learning-based solutions have outperformed humans on selected benchmark datasets, yielding a promising future for scientific and real-world applications. Training of deep learning models requires vast amounts of high quality data to achieve such supreme performance. In real-world scenarios, obtaining a large, coherent, and properly labeled dataset is a challenging task. This is especially true in medical applications, where high-quality data and annotations are scarce and the number of expert annotators is limited. In this paper, we investigate the impact of corrupted ground-truth masks on the performance of a neural network for a brain tumor segmentation task. Our findings suggest that a) the performance degrades about 8% less than it could be expected from simulations, b) a neural network learns the simulated biases of annotators, c) biases can be partially mitigated by using an inversely-biased dice loss function.
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Content available remote License plate detection with machine learning without using number recognition
EN
In autonomous driving, detecting vehicles together with their parts, such as a license plate is important. Many methods with using deep learning detect the license plate based on number recognition. However, there is an idea that the method using deep learning is difficult to use for autonomous driving because of the complexity in realizing deterministic verification. Therefore, development of a method that does not use deep learning (DL) has become important again. Although the authors have made the world's best performance in 2018 for Caltech data with using DL, this concept has now turned to another research without using DL. The CT5L method is the latest type, that includes techniques of the continuity of vertical and horizontal black-and-white pixel values inside the plate, unique Hough transform, only vertical and horizontal lines are detected, the top five in the order of the number of votes to ensure good performance. In this paper, a method to determine the threshold value for binarizing input by machine learning is proposed, and good results are obtained. The detection rate is improved by about 20 points in percent as compared to the fixed case. It achieves the best performance among the conventional fixed threshold method, Otsu's method, and the conventional method of JavaANPR.
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EN
Many industrial machine vision problems, particularly real-time control of manufacturing processes such as laser cladding, require robust and fast image processing. The inherent disturbances in images acquired during these processes makes classical segmentation algorithms uncertain. Among many convolutional neural networks introduced recently to solve such difficult problems, U-Net balances simplicity with segmentation accuracy. However, it is too computationally intensive for usage in many real-time processing pipelines. In this work we present a method of identifying the most informative levels of detail in the U-Net. By only processing the image at the selected levels, we reduce the total computation time by 80%, while still preserving adequate quality of segmentation.
EN
This article presents a novel approach to segmentation and counting of objects in color digital images. The objects belong to a certain class, which in this case are honey bees. The authors briefly present existing approaches which use Convolutional Neural Networks to solve the problem of image segmentation and object recognition. The focus however is on application of U-Net convolutional neural network in an environment where knowledge about the object of interest is only limited to its rough, single pixel location. The authors provide full access to the details of the code used to implement the algorithms, as well as the data sets used and results obtained. The results show an encouraging low level of counting error at 14.27% for the best experiment.
19
Content available remote On effectiveness of human cell nuclei detection dependin
EN
The paper presents results of research on effectiveness of automated detection of human body cells nuclei depending on the digital image color representation used. The problem importance is presented, data representation and processing problems are discussed. The standardized machine vision-based nuclei detection procedure is proposed. Nuclei detection effectiveness measurement algorithm is presented and results are discussed. The conclusion is drawn and future work areas are indicated.
PL
W artykule przedstawiono wyniki badań skuteczności zautomatyzowanego wykrywania jąder komórkowych, w zależności od zastosowanej reprezentacji koloru przetwarzanego obrazu. Przedstawiono problemy związane z przetwarzaniem cyfrowych obrazów medycznych. Zaproponowano ujednoliconą procedurę komputerowego przetwarzania obrazu. Przedstawiono algorytm pomiaru skuteczności wykrywania jąder komórkowych w zależności od zastosowanej przestrzeni barw. Omówiono wyniki, sformułowano wnioski i wskazano przyszłe obszary badań.
20
EN
This work extends the dynamic programming approach to calculation of an elastic metric between two curves to finding paths in pairs of graph drawings that are closest under this metric. The new algorithm effectively solves this problem when all paths between two given nodes in one of these graphs have the same length. It is then applied to the problem of pattern recognition constrained by a superpixel segmentation. Segmentations of test images, obtained without statistical modeling given two shape endpoints, have good accuracy.
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