Retinal vessel segmentation based on phase congruence and GLCM sum-entropy

Mapayi, Temitope; Viriri, Serestina; Tapamo, Jules-Raymond

doi:10.1109/icit.2015.7125352

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…The grey-scale mean, variance and skewness of superpixels are taken as greyscale features, features extracted by grey-level run length matrix of superpixels are taken as texture features. All of these features are important image features and used widely in image analysis [43][44][45]. In Table 2, we list the performances of these methods on segmentation of lung CT images with seven types of interstitial lung diseases.…”

Section: Results Analysismentioning

confidence: 99%

Novel superpixel‐based algorithm for segmenting lung images via convolutional neural network and random forest

2020

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Accurately segmenting lungs from CT images is a fundamental step for quantitative analysis of lung diseases. However, it is still a challenging task because of some interferential factors, such as juxta-pleural nodules, pulmonary inflammation, as well as individual anatomical varieties. In this study, with the combination of a superpixel approach and a hybrid model composed of convolutional neural network and random forest (CNN-RF), the authors propose a novel algorithm to segment lungs from CT images in an automatic and accurate fashion. The authors' lung segmentation covers three main stages: image preprocessing, lung segmenting and segmentation refining. A lung CT image denoised with a fractional-order grey similarity approach is first segmented to a set of superpixels, and the CNN-RF model is then employed to classify the superpixels and identify lungs from the CT image. The segmentation result is further refined by separating the left and right lungs, eliminating trachea, and correcting lung contours. Experiments show that their algorithm can generate more accurate lung segmentation results with 94.98% Jaccard's index and 97.99% Dice similarity coefficient, compared with ground truths, and it achieved better results compared with several feature-based machine learning techniques and current methods on lung segmentation.

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Section: Results Analysismentioning

confidence: 99%

Novel superpixel‐based algorithm for segmenting lung images via convolutional neural network and random forest

2020

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“…Global thresholding techniques have also been implemented for the segmentation of retinal vascular network in [16]. It was however discovered that global thresholding techniques are inefficient for the segmentation of retinal vascular networks [17,18] due to complications in the retinal image characteristics such as width variances in the vascular networks, relatively low contrast of the small-width vascular networks to the retina image background and non-homogeneous illumination noise [10,11]. This is because global thresholding techniques are efficient on the image histograms that have distinct multimodal distribution but inefficient for image histograms with unimodal distribution.…”

Section: Introductionmentioning

confidence: 99%

Automated Detection and Tortuosity Characterization of Retinal Vascular Networks

Mapayi

Owolawi

Adio

2021

JBBBE

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Automated retinal vascular network detection and analysis using digital retinal images continue to play a major role in the field of biomedicine for the diagnosis and management of various forms of human ailments like hypertension, diabetic retinopathy, retinopathy of prematurity, glaucoma and cardiovascular diseases. Although several literature have implemented different automatic approaches of detecting blood vessels in the retinal and also determining their tortuous states, the results obtained show that there are needs for further investigation on more efficient ways to detect and characterize the blood vessel network tortuosity states. This paper implements the use of an adaptive thresholding method based on local spatial relational variance (LSRV) for the detection of the retinal vascular networks. The suitability of a multi-layer perceptron artificial neural network (MLP-ANN) technique for the tortuosity characterization of retinal blood vascular networks is also presented in this paper. Some vessel geometric features of detected vessels are fed into ANN classifier for the automatic classification of the retinal vascular networks as being tortuous vessels or normal vessels. Experimental studies conducted on DRIVE and STARE databases show that the vascular network detection results obtained from the method implemented in this paper detects large and thin vascular networks in the retina. In comparison to preious methods in the literature, the proposed method for vascular network segmentation achieved better performance than several methods in the literature with a mean accuracy value of 95.04% and mean sensitivity value of 75.16% on DRIVE and mean accuracy value of 94.02% and average sensitivity value of 76.55% on STARE with computational processing time of 4.5 seconds and 9.4 seconds on DRIVE and STARE respectively. The MLP-ANN method proposed for the vascular network tortuosity characterization achieves promising accuracy rates of 77.5%, 80%, 83.33%, 85%, 86.67% and 100% for varying training sample sizes.

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Multiscale Fuzzy-Entropy Approach for Vascular Network Delineation in Retinal Images

Mapayi

Owolawi

2021

Proceedings of Sixth International Congress on Information and Communication Technology

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Retinal vessel segmentation based on phase congruence and GLCM sum-entropy

Cited by 6 publications

References 18 publications

Novel superpixel‐based algorithm for segmenting lung images via convolutional neural network and random forest

Novel superpixel‐based algorithm for segmenting lung images via convolutional neural network and random forest

Automated Detection and Tortuosity Characterization of Retinal Vascular Networks

Multiscale Fuzzy-Entropy Approach for Vascular Network Delineation in Retinal Images

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