Unsupervised Segmentation of SAR Images Using Gaussian Mixture-Hidden Evidential Markov Fields

Boudaren, Mohamed El Yazid; An, Lin; Pieczynski, Wojciech

doi:10.1109/lgrs.2016.2615647

Cited by 11 publications

(4 citation statements)

References 25 publications

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“…Evidence Theory, also called Dempster-Shafer envidence theory or belief function, is widely used in image analysis (Boudaren et al 2016, Liu et al 2018 and data classification (Liu et al 2018a(Liu et al , 2018b Definite ω as variable value on set…”

Section: Evidence Theorymentioning

confidence: 99%

EFNet: evidence fusion network for tumor segmentation from PET-CT volumes

Diao¹,

Jiang²,

Han³

et al. 2021

Phys. Med. Biol.

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Precise delineation of target tumor from positron emission tomography-computed tomography (PET-CT) is a key step in clinical practice and radiation therapy. PET-CT co-segmentation actually uses the complementary information of two modalities to reduce the uncertainty of single-modal segmentation, so as to obtain more accurate segmentation results. At present, the PET-CT segmentation methods based on fully convolutional neural network (FCN) mainly adopt image fusion and feature fusion. The current fusion strategies do not consider the uncertainty of multi-modal segmentation and complex feature fusion consumes more computing resources, especially when dealing with 3D volumes. In this work, we analyze the PET-CT co-segmentation from the perspective of uncertainty, and propose evidence fusion network (EFNet). The network respectively outputs PET result and CT result containing uncertainty by proposed evidence loss, which are used as PET evidence and CT evidence. Then we use evidence fusion to reduce uncertainty of single-modal evidence. The final segmentation result is obtained based on evidence fusion of PET evidence and CT evidence. EFNet uses the basic 3D U-Net as backbone and only uses simple unidirectional feature fusion. In addition, EFNet can separately train and predict PET evidence and CT evidence, without the need for parallel training of two branch networks. We do experiments on the soft-tissue-sarcomas and lymphoma datasets. Compared with 3D U-Net, our proposed method improves the Dice by 8% and 5% respectively. Compared with the complex feature fusion method, our proposed method improves the Dice by 7% and 2% respectively. Our results show that in PET-CT segmentation methods based on FCN, by outputting uncertainty evidence and evidence fusion, the network can be simplified and the segmentation results can be improved.

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Section: Evidence Theorymentioning

confidence: 99%

EFNet: evidence fusion network for tumor segmentation from PET-CT volumes

Diao¹,

Jiang²,

Han³

et al. 2021

Phys. Med. Biol.

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show abstract

“…The theory of belief functions (BFT) is also known as the Dempster-Shafer theory or evidence theory [48]. It has been successfully applied in diverse fields, including image analysis [49]- [52], and data classification/clustering [53]- [59], etc.…”

Section: Theory Of Belief Functionsmentioning

confidence: 99%

Joint Tumor Segmentation in PET-CT Images Using Co-Clustering and Fusion Based on Belief Functions

Lian

Ruan

Denœux

et al. 2019

IEEE Trans. on Image Process.

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Precise delineation of target tumor is a key factor to ensure the effectiveness of radiation therapy. While hybrid positron emission tomography-computed tomography (PET-CT) has become a standard imaging tool in the practice of radiation oncology, many existing automatic/semi-automatic methods still perform tumor segmentation on mono-modal images. In this paper, a co-clustering algorithm is proposed to concurrently segment 3-D tumors in PET-CT images, considering that the two complementary imaging modalities can combine functional and anatomical information to improve segmentation performance. The theory of belief functions is adopted in the proposed method to model, fuse, and reason with uncertain and imprecise knowledge from noisy and blurry PET-CT images. To ensure reliable segmentation for each modality, the distance metric for the quantification of clustering distortions and spatial smoothness is iteratively adapted during the clustering procedure. On the other hand, to encourage consistent segmentation between different modalities, a specific context term is proposed in the clustering objective function. Moreover, during the iterative optimization process, clustering results for the two distinct modalities are further adjusted via a belief-functions-based information fusion strategy. The proposed method has been evaluated on a dataset consisting of twenty-one paired PET-CT images for non-small cell lung cancer (NSCLC) patients. The quantitative and qualitative evaluations show that our proposed method performs well compared with the state-of-the-art methods.

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“…Object-oriented classification, as a novel classification approach, can extract features, such as texture, shape and spatial information, after image segmentation which would suppress noise and improve the classification accuracy [30] . Some statistical models and segmentation methods for SAR data have been studied [31][32][33] . However, due to the high dynamics and local statistical properties of SAR images, there are still limitations in SAR image segmentation.…”

Section: Introduction mentioning

confidence: 99%

Integration of optical and SAR remote sensing images for crop-type mapping based on a novel object-oriented feature selection method

Cui

Zhang

Wang

et al. 2020

International Journal of Agricultural and Biological Engineering

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Remote sensing is an important technical means to investigate land resources. Optical imagery has been widely used in crop classification and can show changes in moisture and chlorophyll content in crop leaves, whereas synthetic aperture radar (SAR) imagery is sensitive to changes in growth states and morphological structures. Crop-type mapping with a single type of imagery sometimes has unsatisfactory precision, so providing precise spatiotemporal information on crop type at a local scale for agricultural applications is difficult. To explore the abilities of combining optical and SAR images and to solve the problem of inaccurate spatial information for land parcels, a new method is proposed in this paper to improve crop-type identification accuracy. Multifeatures were derived from the full polarimetric SAR data (GaoFen-3) and a high-resolution optical image (GaoFen-2), and the farmland parcels used as the basic for object-oriented classification were obtained from the GaoFen-2 image using optimal scale segmentation. A novel feature subset selection method based on within-class aggregation and between-class scatter (WA-BS) is proposed to extract the optimal feature subset. Finally, crop-type mapping was produced by a support vector machine (SVM) classifier. The results showed that the proposed method achieved good classification results with an overall accuracy of 89.50%, which is better than the crop classification results derived from SAR-based segmentation. Compared with the ReliefF, mRMR and LeastC feature selection algorithms, the WA-BS algorithm can effectively remove redundant features that are strongly correlated and obtain a high classification accuracy via the obtained optimal feature subset. This study shows that the accuracy of crop-type mapping in an area with multiple cropping patterns can be improved by the combination of optical and SAR remote sensing images.

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Unsupervised Segmentation of SAR Images Using Gaussian Mixture-Hidden Evidential Markov Fields

Cited by 11 publications

References 25 publications

EFNet: evidence fusion network for tumor segmentation from PET-CT volumes

EFNet: evidence fusion network for tumor segmentation from PET-CT volumes

Joint Tumor Segmentation in PET-CT Images Using Co-Clustering and Fusion Based on Belief Functions

Integration of optical and SAR remote sensing images for crop-type mapping based on a novel object-oriented feature selection method

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