Strided fully convolutional neural network for boosting the sensitivity of retinal blood vessels segmentation

Soomro, Toufique Ahmed; Afifi, Ahmed J.; Gao, Junbin; Hellwich, Olaf; Zheng, Lihong; Paul, Manoranjan

doi:10.1016/j.eswa.2019.05.029

Cited by 88 publications

(42 citation statements)

References 43 publications

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“…Table 2 summarizes the results of ResWnet evaluated on the two datasets and compares it with other state-of-the-art methods. It can be seen that the prominent performance of Soomro et al [18,22] is largely attributed to a very complex dedicated pre-processing and post-processing. It enhances the segmentation of the small vessels significantly.…”

Section: B Comparison Results With U-netmentioning

confidence: 99%

ResWnet for Retinal Small Vessel Segmentation

et al. 2020

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U-Net shows excellent performance in biomedical image segmentation tasks, Variants of U-Net has been proposed, such as U-Net with residual blocks, densely connected convolution, or deformed convolution. However, contracting path of these variants is contained four consecutive downsampling layers and an encoding-decoding structure with skip connections. The features map transformation paths are limited. Therefore, ResWnet is proposed in this work, the number of downsampling layers of contracting path is modified to two and the network is changed from the encoding-decoding structure to the encoding-decoding-encoding-decoding structure so that the network can retain more detailed features and extract deeper semantic information. The skip connections are used between the contracting path and the expansion path at the same scale to increase the information transmission path. The convergence speed of the network is accelerated by the residual block replacing convolution layer. The image is scaled to different scales and is inputted into the network from the left to enhance the sensitivity of the network for blood vessels at different scales. The feature maps extracted from different layers of the network are scaled to the same size as the inputted image for fusion and segmentation. The performance of ResWnet is evaluated on DRIVE and STARE databases. The AUC is 0.9799 and 0.9863, and the accuracy is 0.9554 and 0.9723, respectively. According to the results, ResWnet outperforms many other proposed methods in small vessel segmentation. For future research, It can be learned that pre-processing and post-processing are vital to the segmentation accuracy. Furthermore, ResWnet can also be applied to other semantic segmentation tasks such as lung nodule segmentation or cancer cell segmentation. For future research, the preprocessing and post-processing will be tried to further enhance the segmentation accuracy performance of ResWnet.

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Section: B Comparison Results With U-netmentioning

confidence: 99%

ResWnet for Retinal Small Vessel Segmentation

et al. 2020

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“…For instance, the medical images which contain registered CT and MRI are publically distributed by the Harvard Medical school at http://www.med.harvard.edu/AANLIB/home.html, and McConnel Brain Imaging Centre of the Montreal Neurological Institute has distributed datasets at http://www.mouldy.bic.mni.mcgill.ca/brainweb. These datasets have been applied in several image fusion areas [38][39][40][41]. The datasets for infrared and visible imaging are taken from http://www.metapix.de/indexp.htm, http://ece.lehih.edu/SPCRL/IF/image_fusion.html, and http://web.media.mit.edu/~raskar/NPAR04/.…”

Section: A Datasetsmentioning

confidence: 99%

An Enhanced Image Fusion Algorithm by Combined Histogram Equalization and Fast Gray Level Grouping Using Multi-Scale Decomposition and Gray-PCA

Bhutto

et al. 2020

IEEE Access

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Image enhancement is a challenging task in image analysis particularly, it is more challenging in performing image fusion. Image fusion is the process of combining multiple images to produce quality output without any variation in contrast, blurring, and noise. Many image fusion algorithms have been implemented, but their final fused images suffer from variations in background contrast, uneven illumination, blurring, and the presence of noise. To overcome the aforementioned issues, this paper proposed a new image fusion method, which improves image contrast and also gives appropriate details of the image. Our method is based on a set of conventional techniques such as amalgamated histogram equalization and fast gray-scale grouping to handle the problems mentioned, and we improve overall fusion strategies by proposing a novel principal component analysis technique to convert RGB types images to high gray-scale contrast image as the final output image. We have carried out many experiments on different common databases used by various researchers. Our proposed method gives good subjective and objective performances compared to other statuses. Our proposed method can be used in different monitoring applications. INDEX TERMS Histogram equalization and fast gray-level grouping (HEFGLG), Image Fusion, Multifocus image, Infrared image, Visible image, Average pixel intensity, Cross Correlation.

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“…Some recent proposed unsupervised approaches can be roughly divided into matching filter methods [2], vascular tracing methods [3], level set methods [4], model-based method [5], hierarchical image matting model [6], etc. Generally, although unsupervised algorithms improve segmentation performance, thin vessels which affect the whole performance considerably is difficult to be detected [7].…”

Section: Related Workmentioning

confidence: 99%

“…To test the effectiveness of the proposed framework, we compared the output of our approach with several advanced algorithms U-Net [3] [1], AG-UNet [4] [34], IterNet [5] [26], DenseNet [6] [35], V-GAN [7] [36] on STARE and DRIVE. Their segmentation results are obtained by running publicly available codes.…”

Section: Comparison With Other Network Modelsmentioning

confidence: 99%

Retinal vessel segmentation with constrained-based nonnegative matrix factorization and 3D modified Attention U-Net

Yao

Zhu

2020

Preprint

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Due to the complex morphology and characteristic of retinal vessels, it remains challenging for most of the existing algorithms to accurately detect them. This paper proposes a supervised retinal vessels extraction scheme using constrained-based Nonnegative Matrix Factorization (NMF) and 3D modified Attention U-Net architecture. The proposed method detects the retinal vessels by three major steps. First, we perform Gaussian filter and Gamma correction on the Green channel of retinal images to suppress background noise and adjust the contrast of images. Then, the study develops a new within-class and between-class constrained NMF algorithms to extract neighborhood feature information of every pixel and reduce feature data dimension. By using these constraints, the method can effectively gather similar features within-class and discriminate features between-class to improve feature description ability for each pixel. Next, this study formulates segmentation task as a classification problem and solves it with a more contributing 3D modified Attention U-Net as a two-labels classifier for reducing computational cost. This proposed network contains an up-sampling to raise image resolution before encoding and revert image to its original size with a down-sampling after three max-pooling layers. Besides, the Attention Gate (AG) set in these layers contributes to more accurate segmentation by maintaining details while suppressing noises. Finally, the experimental results on two publicly available datasets STARE and DRIVE demonstrate better performance than most existing methods.

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Strided fully convolutional neural network for boosting the sensitivity of retinal blood vessels segmentation

Cited by 88 publications

References 43 publications

ResWnet for Retinal Small Vessel Segmentation

ResWnet for Retinal Small Vessel Segmentation

An Enhanced Image Fusion Algorithm by Combined Histogram Equalization and Fast Gray Level Grouping Using Multi-Scale Decomposition and Gray-PCA

Retinal vessel segmentation with constrained-based nonnegative matrix factorization and 3D modified Attention U-Net

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