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1

An attempt to landscape segmentation based on remote sensing LULC classification: a case study of Khmelnytskyi Oblast, Ukraine

Będkowski Krzysztof, Komarova Uliana

Teledetekcja Środowiska

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2026

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T. 66

1--20

EN

Satellite imagery provides an effective tool for large-scale analysis and supports physico-geographical regionalization. In this study, we investigated the possibility of delineating the landscape of the Khmelnytskyi Oblast (Ukraine) into spatial units based on the results of unsupervised classification of Landsat 8 imagery. Five land cover classes were identified: bare soil, forests, shrublands, agricultural crops, and water bodies. The study area was subsequently divided into 6 × 6 km grid cells, and the proportion of each land cover class within each cell was calculated. In addition, three landscape metrics were determined: IJI, DIVISION, and SHDI. In total, five variables describing land cover proportions and three additional variables describing the spatial structure of their distribution within each grid cell were obtained. The analyses were conducted in five variants using five, eight, and two variables. To reduce interdependencies between variables and to obtain an alternative representation of the data, principal component analysis (PCA) was applied, transforming the variables into a set of uncorrelated components. The transformed variables were then used to perform spatial segmentation using a selected segmentation algorithm. The approximate number of 17 segments was not treated as an optimization target, but rather as a reference level enabling comparison between the independently obtained segmentation results and the physical-geographical regionalization of Khmelnytskyi Oblast proposed by Herenchuk (1980). Parameter selection aimed to achieve a comparable level of spatial detail rather than reproduce the reference division. The obtained spatial divisions showed similarity to the reference regionalization. The results were influenced both by the segmentation algorithm parameters and by the number of variables used in the analysis. Full agreement between our segmentation and the reference regionalization was not possible because the reference regionalization also considered physical-geographical features not reflected in the structure and texture of the satellite imagery. We demonstrated that segmentation in the proposed approach, especially when supplemented with additional data, can be used for the delineation of spatial landscape units.

PL

Obrazy satelitarne stanowią skuteczne narzędzie do analiz wielkoobszarowych oraz wspierają regionalizację fizycznogeograficzną. W tym studium badaliśmy możliwość wykonania podziału krajobrazu Obwodu Chmielnickiego (Ukraina) na jednostki przestrzenne na podstawie wyników nienadzorowanej klasyfikacji obrazów Landsat 8. Zidentyfikowaliśmy pięć klas pokrycia terenu: gleby bez pokrywy roślinnej, lasy, zakrzaczenia, uprawy rolne oraz wody. Następnie analizowany obszar podzielono na kwadraty o wielkości 6 × 6 km i określono udział każdej z klas pokrycia terenu. Ponadto wyznaczyliśmy trzy metryki krajobrazowe: IJI, DIVISION oraz SHDI. Łącznie uzyskaliśmy pięć zmiennych opisujących udziały form pokrycia terenu oraz trzy dodatkowe zmienne opisujące strukturę przestrzenną rozmieszczenia tych klas w każdym z kwadratów. Analizy prowadziliśmy w pięciu wariantach, z wykorzystaniem pięciu, ośmiu oraz dwóch zmiennych. W celu zmniejszenia współzależności między zmiennymi oraz uzyskania alternatywnej reprezentacji danych zastosowano analizę głównych składowych (PCA), przekształcając zmienne do postaci nieskorelowanych komponentów. Przekształcone zmienne wykorzystaliśmy do wykonania podziału przestrzeni za pomocą wybranego algorytmu segmentacji. Przybliżona liczba 17 segmentów nie była celem optymalizacji, lecz punktem odniesienia umożliwiającym porównanie wyników niezależnie uzyskanej segmentacji z regionalizacją fizyczno-geograficzną obwodu chmielnickiego według Herenchuka (1980). Dobór parametrów miał na celu osiągnięcie porównywalnego poziomu szczegółowości przestrzennej, a nie odtworzenie podziału referencyjnego. Uzyskaliśmy podziały przestrzeni wykazujące podobieństwo do regionalizacji referencyjnej. Wpływ na uzyskiwane wyniki miały zarówno parametry algorytmu segmentacji, jak i liczba zastosowanych zmiennych. Uzyskanie pełnej zgodności między naszą segmentacją a regionalizacją referencyjną nie było możliwe, ponieważ regionalizacja ta uwzględniała również cechy fizycznogeograficzne niewidoczne w strukturze i teksturze obrazu satelitarnego. Wykazaliśmy, że segmentacja w zaproponowanym przez nas ujęciu, a także wzbogacona o inne dane, może być wykorzystywana do wyodrębniania przestrzennych jednostek krajobrazowych.

2

Supervised learning for lower urinary tract identification with limited and imbalanced training data

Wajman Radosław, Banasiak Robert, Sputowski Maciej, Mosorov Volodymyr

Przegląd Elektrotechniczny

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2025

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R. 101, nr 6

98--107

EN

In recent years, the advantages of AI in data processing and analysis have become increasingly evident in the medical field. There has been a rapid growth in the application of AI in clinical medicine, including its use in urinary system disease detection. AI offers the capability to process and utilize diagnostic information, presenting new opportunities for precise and individualized treatment while promoting non-invasive diagnostic and therapeutic approaches. This paper introduces an original approach to addressing the supervised learning problem in convolutional neural network (CNN) models for lower urinary tract identification when confronted with a scarce and imbalanced training dataset. The proposed solution involves significantly expanding the diagnostic urinary bladder dataset by increasing the number of samples through various augmentation strategies. However, this approach also intensifies the computational complexity of AI training, rendering it infeasible to load all training datasets into memory simultaneously. To overcome this challenge, a distributed computing approach has been developed, incorporating dynamic loading of training data alongside programming-level memory optimization.

PL

W ostatnich latach zalety sztucznej inteligencji (AI) w przetwarzaniu danych i diagnostyce medycznej stały się coraz bardziej widoczne. Wpływ na to mają postępy w technologii komputerowej i integracja wielu dyscyplin. Warto zauważyć, że nastąpił szybki wzrost zastosowań AI w medycynie klinicznej, w tym jej wykorzystania w technologiach wykrywania chorób układu moczowego. AI oferuje możliwość przetwarzania informacji diagnostycznych, co stwarza nowe możliwości precyzyjnego, spersonalizowanego leczenia, jednocześnie promując nieinwazyjne podejścia diagnostyczne. W niniejszym artykule przedstawiono oryginalne podejście do rozwiązania problemu uczenia nadzorowanego w modelach sieci neuronowych splotowych (CNN) do identyfikacji dolnych dróg moczowych w przypadku ograniczonego i niezrównoważonego zestawu danych treningowych. Proponowane rozwiązanie obejmuje znaczne rozszerzenie zestawu danych diagnostycznych dotyczących pęcherza moczowego poprzez zwiększenie liczby próbek za pomocą różnych strategii. Jednak podejście to zwiększa również złożoność obliczeniową treningu AI, co sprawia, że niewykonalne jest jednoczesne załadowanie wszystkich zestawów danych treningowych do pamięci. Aby przezwyciężyć to wyzwanie, opracowano podejście rozproszonego przetwarzania, obejmujące dynamiczne ładowanie danych wraz z optymalizacją pamięci.

3

Glaucoma detection with the segmentation of optic disc and cup region using improved u-net and classification algorithm

Krishnamoorthy A., Logeswari S., Sivanantham S., Akshaya V., Ganagavalli K., Mohanraj V.

Przegląd Elektrotechniczny

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2025

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R. 101, nr 5

71--76

EN

A major contributor to irreversible blindness worldwide, glaucoma affects millions of people each year. To stop vision loss, early detection through precise diagnosis is crucial. In this work, we offer a hybrid deep learning approach for glaucoma prediction that combines ensemble learning approaches with convolutional neural networks (CNNs). The method combines the use of MobileNet V2 and ResNet-18 for classification with an enhanced U-Net model that incorporates residual connections and attention mechanisms for image segmentation. To increase prediction accuracy, our hybrid system makes use of the advantages of both optic cup-disc segmentation and CNN-based classifiers. To maintain computational efficiency and compliance with data privacy requirements, the model is optimized through the use of sophisticated techniques like as AdamW, cyclic learning rate schedulers, and stochastic weight averaging. In the present paper, we have analyzed the various performance metrics like accuracy, sensitivity, specificity, dice coefficient, Jaccard index, F1 score for the conventional methods, and Res-U-Net Architecture (Improved U-Net). Here, Res-U-Net Architecture (Improved U-Net) achieves an accuracy of 0.93 by 80% of the training data. Hybrid deep learning approach for glaucoma prediction that combines ensemble learning approaches with convolutional neural networks (CNNs)

PL

Jaskra, która jest głównym czynnikiem przyczyniającym się do nieodwracalnej ślepoty na całym świecie, dotyka milionów ludzi każdego roku. Aby zapobiec utracie wzroku, kluczowe znaczenie ma wczesne wykrycie poprzez precyzyjną diagnozę. W tej pracy oferujemy hybrydowe podejście do głębokiego uczenia się do przewidywania jaskry, które łączy podejścia do uczenia zespołowego z konwolucyjnymi sieciami neuronowymi (CNN). Metoda łączy w sobie wykorzystanie MobileNet V2 i ResNet-18 do klasyfikacji z ulepszonym modelem U-Net, który obejmuje szczątkowe połączenia i mechanizmy uwagi do segmentacji obrazu. Aby zwiększyć dokładność predykcji, nasz system hybrydowy wykorzystuje zalety zarówno segmentacji tarczy optycznej, jak i klasyfikatorów opartych na CNN. Aby utrzymać wydajność obliczeniową i zgodność z wymogami prywatności danych, model jest optymalizowany dzięki zastosowaniu zaawansowanych technik, takich jak AdamW, cykliczne harmonogramy szybkości uczenia się i stochastyczne uśrednianie wag. Tutaj przeanalizowaliśmy różne wskaźniki wydajności, takie jak dokładność, czułość, swoistość, współczynnik kostki, indeks Jaccarda, wynik F1 dla metod konwencjonalnych i architektura Res-U-Net (Improved U-Net). W tym przypadku architektura Res-U-Net (Improved U-Net) osiąga dokładność 0,93 przy 80% danych treningowych. Hybrydowe podejście do głębokiego uczenia się do przewidywania jaskry, które łączy podejścia do uczenia zespołowego z konwolucyjnymi sieciami neuronowymi (CNN).

4

A Method for Segmenting FLIR Images of Maritime Objects Based on Visual Perception

Pietkiewicz Tadeusz

Biuletyn Wojskowej Akademii Technicznej

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2025

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Vol. 74, No. 1

17--35

EN

In this paper, a modified adaptive grayscale image segmentation method based on human psychovisual phenomena (VPS) is proposed. This method generates a binarization threshold for each pixel separately. Compared to the0classical VPS method, the use of a pixel observation model in the form of concentric circles is proposed. In the final part of the paper, this method was applied to segment preprocessed FLIR (forward looking infra-red) images of maritime objects. The results obtained by this method were compared with the segmentation results of the same images by the Otsu method using mathematical criteria. The study confirmed better segmentation performance using the modified VPS method.

PL

W artykule zaproponowano zmodyfikowaną adaptacyjną metodę segmentacji obrazów w skali szarości opartą na zjawiskach psychowizualnych człowieka (VPS). Metoda ta generuje próg binaryzacji dla każdego piksela osobno. W porównaniu do klasycznej metody VPS zaproponowano zastosowanie modelu obserwacji pikseli w postaci koncentrycznych okręgów. W końcowej części artykułu metoda ta została zastosowana do segmentacji wstępnie przetworzonych obrazów FLIR (forward looking infra-red) obiektów morskich. Wyniki uzyskane tą metodą zostały porównane z wynikami segmentacji tych samych obrazów metodą Otsu przy użyciu kryteriów matematycznych. Badanie potwierdziło lepszą wydajność segmentacji przy użyciu zmodyfikowanej metody VPS.

5

Analysis on grading of lung nodule images with segmentation using u-net and classification with Convolutional Neural Network Fish Swarm Optimization

Sudha R., Uma Maheswari K.M.

Biocybernetics and Biomedical Engineering

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2025

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Vol. 45, iss. 1

90--104

EN

Lung malignant tumors are abnormal growths of cells in the lungs that have the potential to invade nearby tissues and spread to other parts of the body. Early detection of these malignant lung tumors is crucial to avoid complications and improve patient outcomes. However, manual processing consumes time and is a tedious process. This might result in poor estimation on cancer-prognosis, leading the patients into a higher risk of mortality. Many existing literatures have detected the malignant tumors, yet, found certain difficulties with the identification of size, appearance and spread of cancerous-cells in lung region to determine how far it has been occupied. Hence, the present study aims to overcome the existing complications through Deep Learning based Swarm Intelligence Algorithms. Implementation of the proposed work is involved with three stages such as preprocessing, segmentation and classification. Besides, CT scan possess the capability for giving a comprehensive view than X-rays. Data are collected from LIDC-IDRI (Lung Image Database Consortium-Image Database Resource Initiative) with lung CT-images and accomplishes pre-processing by removing noise efficiently using wiener filter. Further, changes in soft tissues of lungs are identified and segmented in the subsequent phase using U-Net and finally classification is performed using CFSO (Convolutional Neural Network Fish Swarm Optimization) to overcome the slight chance of misclassification error as proposed CFSO can lead to more efficient computational processes since FSO algorithms are designed to minimize computational costs while maximizing performance through their metaheuristic nature. This efficiency is particularly beneficial when dealing with large datasets typical in medical imaging, allowing faster processing times without sacrificing accuracy. Hence, amalgamation of CFSO can reduce the number of features, thus speeding up training and inference times. Through the performance assessment, IoU (Intersection over Union) value attained through the analysis is found to be 0.7822. Further, accuracy obtained by the proposed model is 97.80%, recall is 98.49%, precision is 96.8% and F1-score is 97.32%. Findings of the study exhibits the purposefulness of the study in clinical settings by potentially reducing false negatives in lung cancer screening, ultimately improving patient survival rates through earlier detection and treatment.

6

Integration of LIME with segmentation techniques for SVM classification in Sentinel-2 imagery

Yilmaz Elif Ozlem, Uzun Yakup Kaan, Berkan Emre, Kavzoglu Taskin

Advances in Geodesy and Geoinformation

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2025

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Vol. 74, no. 1

art. no. e61, 2025

EN

Recent advancements in remote sensing technology have facilitated the acquisition of images with higher spatial resolution. In response to this rapid technological evolution, the paradigm of OBIA has emerged as a key approach. An essential component of OBIA is image segmentation, where the careful selection of an appropriate segmentation algorithm and its parameters significantly influences the quality of the segmentation output. This study aims to conduct LULC analysis on Sentinel-2 imagery and compare the accuracy of the SVM classifier across different segmentation methods produced by MRS, SLIC, Mean Shift, and Quick Shift algorithms. The selected study area is located in the Marmara region of Turkey and characterized by seven major LULC classes. The segmentation was conducted though four algorithms, with 60 segment features being extracted for each output, considering spectral, textural, and geometric attributes separately. Following the classification process with SVM, overall accuracies of 96.14% for the MRS, 91.00% for the SLIC, 89.95% for the Mean Shift and 87.95% for the Quick Shift approach were estimated. These results underscore the superior performance of the MRS algorithm with significant level of improvement. This high level of accuracy holds significant potential for delivering more dependable and precise outcomes in planning and decision-making processes. Moreover, integrating XAI, specifically the LIME algorithm, enhances the transparency and comprehensibility of classification analysis within the OBIA framework. Features associated with the NIR and SWIR bands were found to have predominantly positive effects. This integration contributes to improved transparency, enabling more informed and reliable decision-making processes.

7

MultiTumor Analyzer (MTA-20–55): A network for efficient classification of detected brain tumors from MRI images

Sahoo Akshya Kumar, Parida Priyadarsan, Panda Manoj Kumar, Muralibabu K., Mohanty Ashima Sindhu

Biocybernetics and Biomedical Engineering

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2024

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Vol. 44, no. 3

617--634

EN

Brain cancer, one of the leading causes of mortality worldwide, is caused by brain tumors. Early diagnosis of tumors and predicting their progression can help doctors to save lives. In this article, we have designed an automated approach for locating and classifying tumors from MRI images. The novelties of the research work include the following two stages: Developing an encoder-decoder type 20-Layered deep neural network (DNN) named MultiTumor Analyzer (MTA-20) with 15 down-sampling layers and 4 up-sampling layers, the segmentation is performed in the initial stage. Here, we have adhered a Leaky ReLU activation function instead of ReLU which learn a parameter with negative values that may have valuable information which is essential specifically for image segmentation. Further, a 55-layered DNN using multistage feature fusion is developed in the second stage of the work for the classification of localized tumors. The classification is performed using developed MultiTumor Analyzer (MTA-55) DNN with Softmax classifier. The efficacy of the designed network is validated using highly cited quantitative measures such as accuracy, sensitivity, specificity, dice similarity coefficient (DSC), precision, and F1-measure. It is observed that the proposed MTA-20 DNN attains the average accuracy, sensitivity, specificity, DSC, and precision of 99.2 %, 94.6 %, 99.3 %, 88 %, and 82.5 % respectively against seven state-of-the-art techniques. Also, it is found that, the proposed MTA-55 DNN provides the overall accuracy, recall, specificity, F1-measure, precision, and DSC of 99.8 %, 99.633 %, 99.844 %, 99.659 %, 99.689 %, and 99.656 % respectively as compared to thirteen state-of-the-art techniques. These results corroborate the superiority of the proposed technique.

8

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.

9

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.

10

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

|

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.

11

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.

12

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

|

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.

13

FractalCovNet architecture for COVID-19 Chest X-ray image Classification and CT-scan image Segmentation

Munusamy Hemalatha, Karthikeyan Jadarajan Muthukumar, Shriram Gnanaprakasam, Thanga Revathi Shanmugakani, Aravindkumar Sekar

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 3

1025--1038

EN

Precise and fast diagnosis of COVID-19 cases play a vital role in early stage of medical treatment and prevention. Automatic detection of COVID-19 cases using the chest X-ray images and chest CT-scan images will be helpful to reduce the impact of this pandemic on the human society. We have developed a novel FractalCovNet architecture using Fractal blocks and U-Net for segmentation of chest CT-scan images to localize the lesion region. The same FractalCovNet architecture is also used for classification of chest X-ray images using transfer learning. We have compared the segmentation results using various model such as UNet, DenseUNet, Segnet, ResnetUNet, and FCN. We have also compared the classification results with various models like ResNet5-, Xception, InceptionResNetV2, VGG-16 and DenseNet architectures. The proposed FractalCovNet model is able to predict the COVID-19 lesion with high F-measure and precision values compared to the other state-of-the-art methods. Thus the proposed model can accurately predict the COVID-19 cases and discover lesion regions in chest CT without the manual annotations of lesions for every suspected individual. An easily-trained and high-performance deep learning model provides a fast way to identify COVID-19 patients, which is beneficial to control the outbreak of SARS-IICOV.

14

Implementation and evaluation of medical imaging techniques based on conformal geometric algebra

Franchini Silvia, Gentile Antonio, Vassallo Giorgio, Vitabile Salvatore

International Journal of Applied Mathematics and Computer Science

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2020

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Vol. 30, no. 3

415--433

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.

15

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

16

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.

17

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.

18

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.

19

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.

20

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.