A Morphological Hessian Based Approach for Retinal Blood Vessels Segmentation and Denoising Using Region Based Otsu Thresholding

Khan, Khan Bahadar; Khaliq, Amir A.; Shahid, Muhammad

doi:10.1371/journal.pone.0158996

Cited by 99 publications

(58 citation statements)

References 43 publications

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“…The sensitivity of this study is better than [45,[47][48][49][50][51][52] whereas its specificity is better than [53] and its accuracy and AUC is respectively more successful than [48] and [45,51] for DRIVE dataset as in Table 4. The sensitivity of this study is more successful than [45,49,51,52,54,55] whereas the performance of its specificity is better than [53,55] and its accuracy and AUC is respectively higher than [48,53,55] and [45,51] for STARE dataset as in Table 5. The performance of the sensitivity of this study is higher than [56] whereas its accuracy is more successful than [57] for CHASE_DB1 dataset as in Table 6.…”

Section: Discussionmentioning

confidence: 94%

Retinal Vessel Segmentation via Structure Tensor Coloring and Anisotropy Enhancement

Nergiz

Akın

2017

Symmetry

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Abstract:Retinal vessel segmentation is one of the preliminary tasks for developing diagnosis software systems related to various retinal diseases. In this study, a fully automated vessel segmentation system is proposed. Firstly, the vessels are enhanced using a Frangi Filter. Afterwards, Structure Tensor is applied to the response of the Frangi Filter and a 4-D tensor field is obtained. After decomposing the Eigenvalues of the tensor field, the anisotropy between the principal Eigenvalues are enhanced exponentially. Furthermore, this 4-D tensor field is converted to the 3-D space which is composed of energy, anisotropy and orientation and then a Contrast Limited Adaptive Histogram Equalization algorithm is applied to the energy space. Later, the obtained energy space is multiplied by the enhanced mean surface curvature of itself and the modified 3-D space is converted back to the 4-D tensor field. Lastly, the vessel segmentation is performed by using Otsu algorithm and tensor coloring method which is inspired by the ellipsoid tensor visualization technique. Finally, some post-processing techniques are applied to the segmentation result. In this study, the proposed method achieved mean sensitivity of 0.8123, 0.8126, 0.7246 and mean specificity of 0.9342, 0.9442, 0.9453 as well as mean accuracy of 0.9183, 0.9442, 0.9236 for DRIVE, STARE and CHASE_DB1 datasets, respectively. The mean execution time of this study is 6.104, 6.4525 and 18.8370 s for the aforementioned three datasets respectively.

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Section: Discussionmentioning

confidence: 94%

Retinal Vessel Segmentation via Structure Tensor Coloring and Anisotropy Enhancement

Nergiz

Akın

2017

Symmetry

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“…Generally, a conventional retinal blood vessel segmentation process is as shown in Figure 1 in form of a flow chart [17]. It consists of conversion of original image to grey-scale image, image enhancement, vessel segmentation and extraction.…”

Section: Related Workmentioning

confidence: 99%

“…Then, the green channel is inverted using the inverted function. The next step is enhancing the grey-scale image using Contrast Limited Adaptive Histogram Equalization (CLAHE) [17] enhancement to reveal the contrast of the vasculature. For CLAHE the distribution parameter used is exponential while the clipLimit parameter value is 0.03.…”

Section: Pre-processingmentioning

confidence: 99%

“…Otsu is an analysis using the threshold that minimizes the withinclass variance of the thresholded black and white [17]. Unwanted region removal is act like a filter that filters small region of noise to improve the accuracy of the proposed method.…”

Section: Segmentationmentioning

confidence: 99%

“…Although the image processing techniques employed in this proposed method is similar to some reported work, the main difference is the sequence of the processing and a few main components different. For example, [17] used Matrix and Eigenvalue approach before thresholded using local Otsu's Thresholding.…”

Section: Segmentationmentioning

confidence: 99%

See 2 more Smart Citations

Morphological and Otsu’s Thresholding-Based Retinal Blood Vessel Segmentation for Detection of Retinopathy

Kipli¹,

Jiris²,

Sahari³

et al. 2018

IJET

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Retinal blood vessel segmentation is crucial as it is the earliest process in measuring various indicators of retinopathy sign such as arterial-venous nicking, and focal arteriolar and generalized arteriolar narrowing. The segmentation can be clinically used if its accuracy is close to 100%. In this study, a new method of segmentation is developed for extraction of retinal blood vessel. In this paper, we present a new automated method to extract blood vessels in retinal fundus images. The proposed method comprises of two main parts and a few subcomponents which include pre-processing and segmentation. The main focus for the segmentation part is two morphological reconstructions which are the morphological reconstructions followed by the morphological top-hat transform. Then the technique to classify the vessel pixels and background pixels is Otsu’s Thresholding. The image database used in this study is the High Resolution Fundus Image Database (HRFID). The developed segmentation method accuracies are 95.17%, 92.06% and 94.71% when tested on dataset of healthy, diabetic retinopathy (DR) and glaucoma patients respectively. Overall, the performance of the proposed method is comparable with existing methods with overall accuracies were more than 90 % for all three different categories: healthy, DR and glaucoma.

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Diffuse and nonlinear imaging of multiscale vascular parameters for in vivo monitoring of preclinical mammary tumors

Karrobi

Tank

Tabassum

et al. 2019

Journal of Biophotonics

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Diffuse optical imaging (DOI) techniques provide a wide‐field or macro assessment of the functional tumor state and have shown substantial promise for monitoring treatment efficacy in cancer. Conversely, intravital microscopy provides a high‐resolution view of the tumor state and has played a key role in characterizing treatment response in the preclinical setting. There has been little prior work in investigating how the macro and micro spatial scales can be combined to develop a more comprehensive and translational view of treatment response. To address this, a new multiscale preclinical imaging technique called diffuse and nonlinear imaging (DNI) was developed. DNI combines multiphoton microscopy with spatial frequency domain imaging (SFDI) to provide multiscale data sets of tumor microvascular architecture coregistered within wide‐field hemodynamic maps. A novel method was developed to match the imaging depths of both modalities by utilizing informed SFDI spatial frequency selection. An in vivo DNI study of murine mammary tumors revealed multiscale relationships between tumor oxygen saturation and microvessel diameter, and tumor oxygen saturation and microvessel length (|Pearson's ρ| ≥ 0.5, P < 0.05). Going forward, DNI will be uniquely enabling for the investigation of multiscale relationships in tumors during treatment.

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A Morphological Hessian Based Approach for Retinal Blood Vessels Segmentation and Denoising Using Region Based Otsu Thresholding

Cited by 99 publications

References 43 publications

Retinal Vessel Segmentation via Structure Tensor Coloring and Anisotropy Enhancement

Retinal Vessel Segmentation via Structure Tensor Coloring and Anisotropy Enhancement

Morphological and Otsu’s Thresholding-Based Retinal Blood Vessel Segmentation for Detection of Retinopathy

Diffuse and nonlinear imaging of multiscale vascular parameters for in vivo monitoring of preclinical mammary tumors

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