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1

Segmentation of multigradation images based on spatial connectivity features

Timchenko Leonid, Kokriatskaia Natalia, Tverdomed Volodymyr, Stetsenko Oleksandr, Kaplun Valentyna, Kolesnytskyj Oleh, Reshetnik Oleksandr, Smailova Saule, Zhunissova Ulzhalgas

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2023

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T. 13, nr 3

47--50

EN

The article aims to study the multi-level segmentation process of images of arbitrary configuration and placement based on features of spatial connectivity. Existing image processing algorithms are analyzed, and their advantages and disadvantages are determined. A method of organizing the process of segmentation of multi-gradation halftone images is developed and an algorithm of actions according to the described method is given.

PL

Artykuł ma na celu zbadanie procesu wielopoziomowego segmentacji obrazów o dowolnej konfiguracji i rozmieszczeniu w oparciu o cechy łączności przestrzennej. Przeanalizowano istniejące algorytmy przetwarzania obrazu oraz określono ich zalety i wady. Opracowano metodę organizacji procesu segmentacji wielogradacyjnych obrazów półtonowych i przedstawiono algorytm działań zgodnie z opisaną metodą.

2

Classification and detection of skin disease based on machine learning and image processing evolutionary models

Bordoloi Dibyahash, Singh Vijay, Kaliyaperumal Karthikeyan, Ritonga Mahyudin, Jawarneh Malik, Kassanuk Thanwamas, Quiñonez-Choquecota Jose

Computer Assisted Methods in Engineering and Science

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2023

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Vol. 30, no. 2

247--256

EN

Skin disorders, a prevalent cause of illnesses, may be identified by studying their physical structure and history of the condition. Currently, skin diseases are diagnosed using invasive procedures such as clinical examination and histology. The examinations are quite effective and beneficial. This paper describes an evolutionary model for skin disease classification and detection based on machine learning and image processing. This model integrates image preprocessing, image augmentation, segmentation, and machine learning algorithms. The experimental investigation makes use of a dermatology data set. The model employs the machine learning methods: the support vector machine (SVM), the k-nearest neighbors (KNN), and random forest algorithms for image categorization and detection. This suggested methodology is beneficial for the accurate identification of skin disease using image analysis. The SVM algorithm achieved an accuracy of 98.8%. The KNN algorithm achieved a sensitivity of 91%. The specificity of KNN was 99%.

3

PCcS-RAU-Net: Automated parcellated Corpus callosum segmentation from brain MRI images using modified residual attention U-Net

Chandra Anjali, Verma Shrish, Raghuvanshi A.S., Kuber Bodhey Narendra

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 2

403--427

EN

Background: The Corpus callosum (Cc) in the cerebral cortex is a bundle of neural fibers that facilitates inter-hemispheric communication. The Cc area and area of its sub-regions (also known as parcels) have been examined as a biomarker for cortical pathology and differential diagnosis in neurodegenerative diseases such as Autism, Alzheimer’s disease (AD), and more. Manual segmentation and parcellation of Cc are laborious and time-consuming. The present work proposes a novel work of automated parcellated Cc (PCc) segmentation that will serve as a potential biomarker to study and diagnose neurological disorders in brain MRI images. Method: In this perspective, the present work aims to develop an automated PCc segmentation from mid-sagittal T1- weighted (w) 2D brain MRI images using a deep learning-based fully convolutional network, a modified residual attention U-Net, referred to as PCcS-RAU-Net. The model has been modified to use a multi-class segmentation configuration with five target classes (parcels): rostrum, genu, mid-body, isthmus and splenium. Results: The experimental research uses two benchmark MRI datasets, ABIDE and OASIS. The proposed PCcS-RAU-Net outperformed existing methods on the ABIDE dataset with a DSC of 97.10% and MIoU of 94.43%. Furthermore, the model’s performance is validated on the OASIS and Real clinical image (RCI) data and hence verifies the model’s generalization capability. Conclusion: The proposed PCcS-RAU-Net model extracts essential characteristics such as the total area of the Cc (TCcA) to categorize MRI slices into healthy controls (HC) and disease groups. Also, sub-regional areas, Cc1A to Cc5A, help study atrophy progression for early diagnosis.

4

A Comparative Analysis of Image Segmentation Using Classical and Deep Learning Approach

Plaksyvyi Arsen, Skublewska-Paszkowska Maria, Powroźnik Paweł

Advances in Science and Technology. Research Journal

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2023

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Vol. 17, no 6

127--139

EN

Segmentation is one of the image processing techniques, widely used in computer vision, to extract various types of information represented as objects or areas of interest. The development of neural networks has influenced image processing techniques, including creation of new ways of image segmentation. The aim of this study is to compare classical algorithms and deep learning methods in RGB image segmentation tasks. Two hypotheses were put forward: 1) “The quality of segmentation applying deep learning methods is higher than using classical methods for RGB images”, and 2) “The increase of the RGB image resolution has positive impact on the segmentation quality”. Two traditional segmentation algorithms (Thresholding and K-means) were compared with deep learning approach (U-Net, SegNet and FCN 8) to verify RGB segmentation quality. Two resolutions of images were taken into consideration: 160x240 and 320x480 pixels. Segmentation quality for each algorithm was estimated based on four parameters: Accuracy, Precision, Recall and Sorensen-Dice ratio (Dice score). In the study the Carvana dataset, containing 5,088 high-resolution images of cars, was applied. The initial set was divided into training, validation and test subsets as 60%, 20%, 20%, respectively. As a result, the best Accuracy, Dice score and Recall for images with resolution 160x240 were obtained for U-Net, achieving 99.37%, 98.56%, and 98.93%, respectively. For the same resolution the highest Precision 98.19% was obtained for FCN-8 architecture. For higher resolution, 320x480, the best mean Accuracy, Dice score, and Precision were obtained for FCN-8 network, reaching 99.55%, 99.95% and 98.85%, respectively. The highest results for classical methods were obtained for Threshold algorithm reaching 80.41% Accuracy, 58.49% Dice score, 67.32% Recall and 52.62% Precision. The results confirm both hypotheses.

5

Integration of feature extraction, attribute combination and image segmentation for object delineation on seismic images

Khayer Keyvan, Hosseini Fard Esmail, Roshandel Kahoo Amin, Soleimani Monfared Mehrdad, Ahmadyfard Alireza

Acta Geophysica

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2023

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Vol. 71, no. 1

275--292

EN

Automatic geological interpretation, specifically modeling salt dome and fault detection, is controversial task on seismic images from complex geological media. In advanced techniques of seismic interpretation and modeling, various strategies are utilized for combination and integration different information layers to obtain an image adequate for automatic extraction of the object from seismic data. Efficiency of the selected feature extraction, data integration and image segmentation methods are the most important parameters that affect accuracy of the final model. Moreover, quality of the seismic data also affects confidence of the selected seismic attributes for integration. The present study proposed a new strategy for efficient delineation and modeling of geological objects on the seismic image. The proposed method consists of extraction specific features by the histogram of oriented gradients (HOG) method, statistical analysis of the HOG features, integration of features through hybrid attribute analysis and image classification or segmentation. The final result is a binary model of the target under investigation. The HOG method here modified accordingly for extraction of the related features for delineation of salt dome and fault zones from seismic data. The extracted HOG parameter then is statically analyzed to define the best state of information integration. The integrated image, which is the hybrid attribute, then is used for image classification, or image segmentation by the image segmentation method. The seismic image labeling procedure performs on the related seismic attributes, evaluated by the extracted HOG feature. Number of HOG feature and the analyzing parameters are also accordingly optimized. The final image classification then is performed on an image which contains all the embedded information on all the related textural conventional and statistical attributes and features. The proposed methods here apply on four seis mic data examples, synthetic model of salt dome and faults and two real data that contain salt dome and fault. Results have shown that the proposed method can more accurately model the targets under investigation, compared to advanced extracted attributes and manual interpretations.

6

DE-XRT coal preparation image overlapping and adhesion particle segmentation method

He Lei, Wang Shuang, Guo Yongcun

Physicochemical Problems of Mineral Processing

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2022

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Vol. 58, iss. 6

art. no. 155840

EN

Accurate segmentation of dual-energy X-ray transmission (DE-XRT) coal and gangue image regions are a prerequisite for feature extraction, identification, localization, and separation. A watershed algorithm based on multi-grayscale threshold segmentation (MGTS) is proposed to mark the foreground for the adhesion and overlap of coal and gangue. The grayscale images of foreground objects are segmented using multiple grayscale thresholds, and the number of connected domains is recorded each time. As the gray threshold value decreases, overlapping and adhering objects are gradually separated. The binary image segmented at the grayscale threshold with the most significant number of connected domains is used as a marker region. This marker region is used as the seed point of the watershed algorithm to find the dividing line. The experimental results show that the segmentation accuracy is 91.35%, and the segmentation accuracy of overlapping adhesions of 2, 3, and 4 targets is higher than 90%.

7

On the applicability of integrated UAV photogrammetry and automatic feature extraction for cadastral mapping

Ajayi Oluibukun Gbenga, Oruma Emmanuel

Advances in Geodesy and Geoinformation

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2022

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Vol. 71, no. 1

art. no. e19, 2022

EN

The applicability of integratedUnmannedAerialVehicle (UAV)-photogrammetry and automatic feature extraction for cadastral or property mapping was investigated in this research paper. Multi-resolution segmentation (MRS) algorithm was implemented on UAVgenerated orthomosaic for mapping and the findings were compared with the result obtained from conventional ground survey technique using Hi-Target Differential Global Positioning System (DGPS) receivers. The overlapping image pairs acquired with the aid of a DJI Mavic air quadcopter were processed into an orthomosaic using Agisoft metashape software while MRS algorithm was implemented for the automatic extraction of visible land boundaries and building footprints at different Scale Parameter (SPs) in eCognition developer software. The obtained result shows that the performance of MRS improves with an increase in SP, with optimal results obtained when the SP was set at 1000 (with completeness, correctness, and overall accuracy of 92%, 95%, and 88%, respectively) for the extraction of the building footprints. Apart from the conducted cost and time analysis which shows that the integrated approach is 2.5 times faster and 9 times cheaper than the conventional DGPS approach, the automatically extracted boundaries and area of land parcels were also compared with the survey plans produced using the ground survey approach (DGPS) and the result shows that about 99% of the automatically extracted spatial information of the properties fall within the range of acceptable accuracy. The obtained results proved that the integration of UAVphotogrammetry and automatic feature extraction is applicable in cadastral mapping and that it offers significant advantages in terms of project time and cost.

8

Graph-based segmentation with homogeneous hue and texture vertices

Ngo Lua, Han Jae-Ho

Optica Applicata

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2021

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Vol. 51, nr 4

541--549

EN

This work presents an automated segmentation method, based on graph theory, which processes superpixels that exhibit spatially similarities in hue and texture pixel groups, rather than individual pixels. The graph shortest path includes a chain of neighboring superpixels which have minimal intensity changes. This method reduces graphics computational complexity because it provides large decreases in the number of vertices as the superpixel size increases. For the starting vertex prediction, the boundary pixel in first column which is included in this starting vertex is predicted by a trained deep neural network formulated as a regression task. By formulating the problem as a regression scheme, the computational burden is decreased in comparison with classifying each pixel in the entire image. This feasibility approach, when applied as a preliminary study in electron microscopy and optical coherence tomography images, demonstrated high measures of accuracy: 0.9670 for the electron microscopy image and 0.9930 for vitreous/nerve-fiber and inner-segment/outer-segment layer segmentations in the optical coherence tomography image.

9

Image segmentation method of mine pass soil and ore based on the fusion of the confidence edge detection algorithm and mean shift algorithm

Jin Feng, Zhan Kai, Chen Shengjie, Huang Shuwei, Zhang Yuansheng

Gospodarka Surowcami Mineralnymi

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2021

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T. 37, z. 4

133--152

EN

In the execution of edge detection algorithms and clustering algorithms to segment image containing ore and soil, ore images with very similar textural features cannot be segmented effectively when the two algorithms are used alone. This paper proposes a novel image segmentation method based on the fusion of a confidence edge detection algorithm and a mean shift algorithm, which integrates image color, texture and spatial features. On the basis of the initial segmentation results obtained by the mean shift segmentation algorithm, the edge information of the image is extracted by using the edge detection algorithm based on the confidence degree, and the edge detection results are applied to the initial segmentation region results to optimize and merge the ore or pile belonging to the same region. The experimental results show that this method can successfully overcome the shortcomings of the respective algorithm and has a better segmentation results for the ore, which effectively solves the problem of over segmentation.

PL

W procesie algorytmu wykrywania krawędzi ufności i algorytmu grupowania do segmentacji obrazu zawierającego rudę i glebę, obraz rudy o bardzo podobnych cechach tekstury nie może być skutecznie segmentowany, gdy oba algorytmy są używane osobno. W pracy zaproponowano nowatorską metodę segmentacji obrazu opartą na połączeniu algorytmu wykrywania krawędzi ufności i algorytmu zmiany średniej, który integruje kolor, teksturę i cechy przestrzenne obrazu. Na podstawie wstępnych wyników segmentacji uzyskanych przez algorytm segmentacji zmiany średniej informacja o krawędziach oryginalnego obrazu jest wyodrębniana za pomocą algorytmu wykrywania krawędzi opartego na stopniu ufności, a otrzymane wyniki są stosowane do początkowych wyników segmentacji obszaru w celu optymalizacji i scalenia rudy lub gleby należących do tego samego obszaru. Wyniki eksperymentalne pokazują, że metoda ta może skutecznie przezwyciężyć wady odpowiedniego algorytmu i daje lepsze wyniki segmentacji dla rudy, co dobrze rozwiązuje problem nadmiernej segmentacji.

10

Segmentation of aggregate and asphalt in photographic images of pavements

Mejía Angela Marcela, Alzate Marco Aurelio, Reyes-Ortiz Oscar Javier

Engineering Transactions

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2021

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Vol. 69, iss. 1

19--42

EN

Particle size distribution of aggregate in asphalt pavements is used for determining important characteristics like stiffness, durability, fatigue resistance, etc. Unfortunately, measuring this distribution requires a sieving process that cannot be done directly on the already mixed pavement. The use of digital image processing could facilitate this measurement, for which it is important to classify aggregate from asphalt in the image. This classification is difficult even for humans and much more for classical image segmentation algorithms. In this paper, an expert committee approach was used, including classical adaptive Otsu, k-means vector quantization over a set of 8 principal components obtained from 26 features, and a Gaussian mixture model whose parameters are estimated through the expectation-maximization algorithm. A novel cellular automata approach is used to coordinate these expert opinions. Finally, a simple heuristic is used to reduce sub- and over-segmentation. The segmentation results are comparable to those obtained by a human expert, while the sieve size of the segmented images corresponds very well with that obtained from the sieving process, validating the proposed method of segmentation. The results show that with the digital imaging procedure it was possible to detect particles with a size of 100 m with 90% of success with respect to time-consuming manual techniques. In addition, with these results it is possible to establish the homogeneity of the sample and the distribution of the particles within the asphalt mixture.

11

Automatic defect detection and characterization by thermographic images based on damage classifiers evaluation

Dinardo Giuseppe, Fabbiano Laura, Tamborrino Rosanna, Vacca Gaetano

Metrology and Measurement Systems

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2020

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Vol. 27, nr 2

219--242

EN

In the framework of non-destructive evaluation (NDE), an accurate and precise characterization of defects is fundamental. This paper proposes a novel method for characterization of partial detachment of thermal barrier coatings from metallic surfaces, using the long pulsed thermography (LPT). There exist many applications, in which the LPT technique provides clear and intelligible thermograms. The introduced method comprises a series of post-processing operations of the thermal images. The purpose is to improve the linear fit of the cooling stage of the surface under investigation in the logarithmic scale. To this end, additional fit parameters are introduced. Such parameters, defined as damage classifiers, are represented as image maps, allowing for a straightforward localization of the defects. The defect size information provided by each classifier is, then, obtained by means of an automatic segmentation of the images. The main advantages of the proposed technique are the automaticity (due to the image segmentation procedures) and relatively limited uncertainties in the estimation of the defect size.

12

The diagnostic of two-phase separation processusing digital image segmentation algorithms

Łukiański Michał, Wajman Radosław

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2020

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T. 10, nr 3

5--8

EN

The project aimedto develop and implement algorithms to diagnose the phase separation process based on digital images. The image processing techniques and various numerical methods for interpolation and integration were used to identify the process state. The swirl’s volume and diametersat its three different levelscan be determined on-line. A consistent diagnostic signal is produced and can be used by the control unit. The program was written in Python using the OpenCV library that allows the analysis of digital images. The article presents thedeveloped procedure that provides reliable results despite the poor quality of theinput source video stream. The complete procedure was described with the results’ presentation and discussionat each step.

PL

Celem projektu było opracowanie oraz implementacja algorytmów pozwalających diagnozować przebieg procesu separacji faz na podstawie obrazów cyfrowych. W kontekście identyfikacji stanu procesu wykorzystano techniki przetwarzania obrazów oraz metody numeryczne interpolacjioraz całkowanianumerycznego. Wyznaczane są charakterystyczne parametry wiru jak objętość oraz średnice na trzech różnych jego wysokościach. Zwracany spójny sygnał diagnostyczny może być dalej wykorzystany przez jednostkę sterującą. Program został napisany w języku Python z wykorzystaniem biblioteki OpenCV pozwalającej na przetwarzanie obrazów cyfrowych.W artykule zaprezentowano opracowaną procedurę, która dostarcza wiarygodnych wyników mimo słabej jakości obrazów wejściowych wynikającej ze złego oświetlenia sceny. Procedura została opisana wraz z prezentacją wyników i dyskusją nakażdym jej etapie.

13

Ferromagnetic Underwater Target Detection Based on a Magnetic Anomaly Map

Bodus-Olkowska Izabela, Uriasz Janusz

Geomatics and Environmental Engineering

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2020

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Vol. 14, no. 1

35--45

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

14

Speckle noise reduction and image segmentation based on a modified mean filter

Arulpandy P., Trinita-Pricilla M.

Computer Assisted Methods in Engineering and Science

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2020

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Vol. 27, no. 4

221--239

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.

15

Stack generalized deep ensemble learning for retinal layer segmentation in Optical Coherence Tomography images

Anoop B. N., Pavan Rakesh, Girish G. N., Kothari Abhishek R., Rajan Jeny

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 4

1343--1358

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.

16

Segmentation of MRI data using multi-objective antlion based improved fuzzy c-means

Singh Munendra, Venkatesh Vishal, Verma Ashish, Sharma Neeraj

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 3

1250--1266

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.

17

Multi-path convolutional neural network in fundus segmentation of blood vessels

Tian Chun, Fang Tao, Fan Yingle, Wu Wei

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 2

583--595

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.

18

Gastric histopathology image segmentation using a hierarchical conditional random field

Sun Changhao, Li Chen, Zhang Jinghua, Rahaman Md Mamunur, Ai Shiliang, Chen Hao, Kulwa Frank, Li Yixin, Li Xiaoyan, Jiang Tao

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 4

1535--1555

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.

19

A survey on image segmentation of blood and bone marrow smear images with emphasis to automated detection of Leukemia

Anilkumar K.K., Manoj V.J., Sagi T.M.

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 4

1406--1420

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.

20

A fast graph-based algorithm for automated segmentation of subcutaneous and visceral adipose tissue in 3D abdominal computed tomography images

Kucybala Iwona, Tabor Zbislaw, Ciuk Szymon, Chrzan Robert, Urbanik Andrzej, Wojciechowski Wadim

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 2

729--739

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.