SAR image segmentation using region growing and spectral cluster

Baghi, Ali; Karami, Alimohammad

doi:10.1109/pria.2017.7983052

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…For portioning lung parenchyma, a versatile power thresholding technique was utilized. Baghi et al [13] Presented another technique dependent on the RG and Spectral Cluster (SC) for the division of synthetic aperture radar pictures. In the proposed technique first RG is applied to the SAR pictures so as to discover the boundary and afterward segmentation is finished utilizing SC strategy.…”

Section: Related Workmentioning

confidence: 99%

Karınca Aslanı Optimizasyon Algoritması Kullanarak Görüntü Segmentasyonu İçin Modifiye Edilmiş Bölge Genişletme Yöntemi

Jama¹,

Baykan²

2020

European Journal of Science and Technology

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Image segmentation is a significant step in image processing that applies to various fields. These fields include machine vision, object detection, astronomy, biometric recognition systems (face, fingerprint, plate, and eye), medical imaging, video surveillance, and many other image-based technologies. Efficient image segmentation is one of the most important tasks and critical roles in automatic image processing. Especially in engineering studies, finding the most suitable solutions for problems is one of the important research topics. Bio-inspired algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Bat Algorithm (BAT), etc. are used to find the optimal solutions in search spaces and Ant Lion Optimization (ALO) is one of these algorithms. In recent years, bio-inspired algorithms are used to optimize the segmentation parameters of the images. This research proposes a modified region growing (RG) image segmentation approach using bio-inspired ALO. Region growing (RG) has three main problems as the selection of the right seeds, the number of seeds, and the region growing strategy. Therefore, ALO was used to solve seed selection problems in RG. In this study, firstly, the median filter was applied to the inputs to improve the quality of the images. Subsequently, the region growing segmentation was carried out using optimal seed points obtained from the ALO. For obtaining the optimal seeds, ALO was used to solve the limitations of RG during the segmentation process. The success of the proposed approach was tested using some images taken from the BSDS300 (Berkeley) dataset. The experimental results show that the proposed method segments almost all the images.

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Section: Related Workmentioning

confidence: 99%

Karınca Aslanı Optimizasyon Algoritması Kullanarak Görüntü Segmentasyonu İçin Modifiye Edilmiş Bölge Genişletme Yöntemi

Jama¹,

Baykan²

2020

European Journal of Science and Technology

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“…Specifically, this approach iteratively analyzes the neighboring initial-seed-point pixels and determines whether the pixel neighbors ought to be added to the region. It has been used for several segmentation tasks including retinal-vessel segmentation [18], vessel-segmentation algorithm based on spectrum information [19], and color image segmentation and for segmentation of synthetic aperture radar [20,21].…”

Section: Lymph-node Segmentationmentioning

confidence: 99%

Semi-automatic lymph node segmentation and classification using cervical cancer MR imaging

Bnouni

Mechi

Rekik

et al. 2018

2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

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The segmentation and classification of Lymph Nodes (LNs) is a fundamental but challenging step in the analysis of medical images of cervical cancer. Both tasks can leverage morphological features such as size, shape, contour, and heterogeneous appearance. However, these features might vary with the progressive state of LNs. Hence, accurate detection of LNs boundary is an essential step sing to classify LN as suspect (malignant) and non-suspect (benign). However, manual delineation of LNs might produce classification errors due to the inter and intra-observer variability. Semi-automatic and automatic LNs segmentation methods are greatly desired as they would help improve patient diagnosis and treatment processes. Currently, Magnetic Resonance Imaging (MRI) is widely used to diagnose cervical cancer and LN involvement. Diffusion Weighted (DW)-MRI exhibits metastatic LN as bright regions. This paper presents a semi-automatic segmentation and classification method of LNs. Specifically, we propose a novel approach which leverages (1) the complementarity of structural and diffusion MR images through a fusion step and (2) morphological features of the segmented metastatic LNs for classification. The contribution of our proposed algorithm is threefold. First, we fuse the axial T2-Weighted (T2-w) anatomical image and the DW image. Second, we detect LNs using region-growing method in order to compute the final classification. Third, segmentation results are then used to classify LNs based on a gray level dependency matrix technique which extracts LN features. We evaluated our method using 10 MR images T2-w and DW with 47 metastatic LNs. We obtained an average accuracy of 70.21% for cervical cancer nodule classification.

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“…When SAR-based flood extent mapping is the case, many methods and their combinations are used by the researchers. These are, texture analysis (Pradhan et al 2014), region growing (Baghi and Karami, 2017), supervised classification (Benoudjit and Guida, 2019), unsupervised classification (Carincotte et al 2006) and histogram thresholding (Brown et al 2016). Texture analysis is a technique that examines and calculates textures in an image by using the first order and second order statistics.…”

Section: Introductionmentioning

confidence: 99%

Flood Inundation Mapping with Supervised Classifiers: 2021 Gediz Plain Flood

Arslan

Kartal

2023

Turk J Remote Sens GIS

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Generation of flood inundation maps is beneficial in flood risk assessment and evaluation. Flood inundation mapping can be achieved by many remote sensing techniques like change detection (CD) with thresholding and machine learning-based (ML) methods. Optical and synthetic aperture radar (SAR) imagery are widely used, provided by different satellite systems. This study used Sentinel-1 SAR and Sentinel-2 MSI satellite data in Google Earth Engine (GEE) with supervised ML algorithms. Gediz Plain, Turkey was selected as the study area, which is an agricultural area covered mostly by croplands. A flood event that occurred on February 2, 2021, was examined and flood inundation map for the study area was composed. Support Vector Machines (SVM), Random Forest (RF) and K-Nearest Neighbor (KNN) ML algorithms were selected and models were trained with manually created labelled data in GEE. Also, CD was applied on after and before event SAR images in a traditional approach. RF classifier performs best in Sentinel-2 MSI imagery with 94% overall classification accuracy where KNN classifier gives 93.3% accuracy value for Sentinel-1 SAR dataset, indicating the robustness of SAR imagery for all-weather conditions.

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SAR image segmentation using region growing and spectral cluster

Cited by 7 publications

References 12 publications

Karınca Aslanı Optimizasyon Algoritması Kullanarak Görüntü Segmentasyonu İçin Modifiye Edilmiş Bölge Genişletme Yöntemi

Karınca Aslanı Optimizasyon Algoritması Kullanarak Görüntü Segmentasyonu İçin Modifiye Edilmiş Bölge Genişletme Yöntemi

Semi-automatic lymph node segmentation and classification using cervical cancer MR imaging

Flood Inundation Mapping with Supervised Classifiers: 2021 Gediz Plain Flood

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