Genetic algorithm matched filter optimization for automated detection of blood vessels from digital retinal images

Al-Rawi, Mohammed; Karajeh, Huda

doi:10.1016/j.cmpb.2007.05.012

Cited by 85 publications

(50 citation statements)

References 7 publications

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“…Two MFs are employed to separately enhance small and thick vessels. Similarly, optimal MF parameters are retrieved using genetic algorithm in [30]. However, PSO has the shortcoming of easily falling into local optima, influencing the segmentation performance.…”

Section: A Unsupervisedmentioning

confidence: 99%

“…Feng et al [20] 2010 Brain MRA Unsupervised machine learning Hassouna et al [21] 2006 Brain MRA (Sec. V-A) Oliveira et al [22] 2011 Liver CT Goceri et al [23] 2017 Liver MRI Bruyninckx et al [24] 2010 Liver CT Bruyninckx et al [25] 2009 Lung CT Asad et al [26] 2017 Retina CFP Mapayi et al [27] 2015 Retina CFP Sreejini et al [28] 2015 Retina CFP Cinsdikici et al [29] 2009 Retina CFP Al-Rawi et al [30] 2007 Retina CFP Hanaoka et al [31] 2015 Brain MRA Supervised machine learning Sironi et al [32] 2014 Brain Microscopy (Sec. V-B) Merkow et al [33] 2016 Cardiovascular and Lung CT and MRI Sankaran et al [34] 2016 Coronary CTA Schaap et al [35] 2011 Coronary CTA Zheng et al [36] 2011 Coronary CT Nekovei et al [37] 1995 Coronary CT Smistad et al [38] 2016 Femoral region, Carotid US Chu et al [39] 2016 Liver X-ray fluoroscopic Orlando et al [40] 2017 Retina CFP Dasgupta et al [41] 2017 Retina CFP Mo et al [42] 2017 Retina CFP Lahiri et al [43] 2017 Retina CFP Annunziata et al [44] 2016 Retina Microscopy Fu et al [45] 2016 Retina CFP Luo et al [46] 2016 Retina CFP Liskowski et al [47] 2016 Retina CFP Li et al [48] 2016 Retina CFP Javidi et al [49] 2016 Retina CFP Maninis et al [50] 2016 Retina CFP Prentasvic et al [51] 2016 Retina CT Wu et al [52] 2016 Retina CFP Annunziata et al [53] 2015 Retina Microscopy Annunziata et al [54] 2015 Retina Microscopy Vega et al [55] 2015 Retina CFP Wang et al …”

Section: Introductionmentioning

confidence: 99%

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Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Moccia

Momi

Hadji

et al. 2018

Computer Methods and Programs in Biomedicine

469

263

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Abstract-Background: Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semiautomatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.g. resolution, noise and vessel contrast).Objective: This paper aims at reviewing the most recent and innovative blood vessel segmentation algorithms. Among the algorithms and approaches considered, we deeply investigated the most novel blood vessel segmentation including machine learning, deformable model, and tracking-based approaches.Method: This paper analyzes more than 100 articles focused on blood vessel segmentation methods. For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed. Benefits and disadvantages of each method are highlighted.Discussion: Despite the constant progress and efforts addressed in the field, several issues still need to be overcome. A relevant limitation consists in the segmentation of pathological vessels. Unfortunately, not consistent research effort has been addressed to this issue yet. Research is needed since some of the main assumptions made for healthy vessels (such as linearity and circular cross-section) do not hold in pathological tissues, which on the other hand require new vessel model formulations. Moreover, image intensity drops, noise and low contrast still represent an important obstacle for the achievement of a high-quality enhancement. This is particularly true for optical imaging, where the image quality is usually lower in terms of noise and contrast with respect to magnetic resonance and computer tomography angiography. Conclusion:No single segmentation approach is suitable for all the different anatomical region or imaging modalities, thus the primary goal of this review was to provide an up to date source of information about the state of the art of the vessel segmentation algorithms so that the most suitable methods can be chosen according to the specific task.

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Section: A Unsupervisedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Moccia

Momi

Hadji

et al. 2018

Computer Methods and Programs in Biomedicine

469

263

View full text Add to dashboard Cite

show abstract

“…After this, by applying morphological operators, the vasculature is filtered from the background for the last and final segmentation. Matched filtering techniques [37][38][39][40][41][42] generally use a 2-D linear structural element with a Gaussian cross-profile section, extruded or rotated into three dimensions for identification of blood vessel cross-profile (typically a Gaussian or Gaussian-derivative profile). The kernel is oriented into many rotations (generally 8 or 12) to adjust into vessels of distinct configuration after this image is thresholded to get the vessel silhouette from the background.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Segmentation Technique for Blood Vessel in Retinal Images

Mehta¹,

Kaur²

2016

IJCA

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One of the major symptoms of many blood related diseases like diabetes or cardiovascular disease is the change in blood vessel features. These diseases can be detected by analyzing features of retinal vessels and proper treatment can be provided to patient in early stages of disease. Cost associated in detecting these changes and inconsistency in the detection procedure led to the automation of this process. Among other tasks, retinal blood vessel segmentation is the foremost and very challenging task from which various features are analyzed to detect the disease. In this paper, an effective blood vessel segmentation method from coloured retinal fundus images is presented. Segmentation is done by extracting the green channel from RGB retinal image. Firstly the vessel structure is estimated using morphological operations and then noise is removed using Rician Denoise method. After removing the noise, segmentation of blood vessels is carried out using thresholding method. Segmented image needs to be post-processed before considering it for examining any disease. Proposed segmentation method was evaluated on two publicly available DRIVE and STARE datasets. Segmentation process achieves high level of accuracy than most of the previous techniques. Further, results have demonstrated that the proposed method is applicable for segmenting retinal vessels and taking measurements from it. Advantages of this method are its simplicity, fast segmentation process, high efficiency and scalability to deal with coloured retinal images of high resolution.

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“…In this method a former assumption is made that the cross section of the vessels can be proximated by a Gaussian function. Al-Rawi et al [4] proposed an algorithm for automatic extraction of blood vessels by optimizing the parameters of the matched filter using genetic algorithm. Matched filter with first order derivative of Gaussian has been elaborated in [5], where the blood vessels are identified by thresholding the retinal image's response to the matched filter and the threshold is adjusted by image response's to the first order derivative of Gaussian.…”

Section: Introductionmentioning

confidence: 99%

Detection of retinal blood vessels from ophthalmoscope images using morphological approach

Dash

Bhoi

2017

ELCVIA

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Accurate segmentation of retinal blood vessels is an essential task for diagnosis of various pathological disorders. In this paper, a novel method has been introduced for segmenting retinal blood vessels which involves pre-processing, segmentation and post-processing. The pre-processing stage enhanced the image using contrast limited adaptive histogram equalization and 2D Gabor wavelet. The enhanced image is segmented using geodesic operators and a final segmentation output is obtained by applying a post-processing stage that involves hole filling and removal of isolated pixels. The performance of the proposed method is evaluated on the publicly available Digital retinal images for vessel extraction (DRIVE) and High-resolution fundus (HRF) databases using five different measurements and experimental analysis shows that the proposed method reach an average accuracy of 0.9541 on DRIVE database and 0.9568, 0.9478 and 0.9613 on HRF database with healthy, diabetic retinopathy (DR) and glaucomatous images respectively.

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Genetic algorithm matched filter optimization for automated detection of blood vessels from digital retinal images

Cited by 85 publications

References 7 publications

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

An Enhanced Segmentation Technique for Blood Vessel in Retinal Images

Detection of retinal blood vessels from ophthalmoscope images using morphological approach

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