Aboul Ella Hassanien (Abo)

The aim was to present a novel automated approach for extracting the vasculature of retinal fundus images. The proposed vasculature extraction method on retinal fundus images consists of two phases: preprocessing phase and segmentation phase. In the first phase, brightness enhancement is applied for the retinal fundus images. For the vessel segmentation phase, a hybrid model of multilevel thresholding along with whale optimization algorithm (WOA) is performed. WOA is used to improve the segmentation accuracy through finding the n−1 optimal n-level threshold on the fundus image. To evaluate the accuracy, sensitivity, specificity, accuracy, receiver operating characteristic (ROC) curve analysis measurements are used. The proposed approach achieved an overall accuracy of 97.8%, sensitivity of 88.9%, and specificity of 98.7% for the identification of retinal blood vessels by using a dataset that was collected from Bostan diagnostic center in Fayoum city. The area under the ROC curve reached a value of 0.967. Automated identification of retinal blood vessels based on whale algorithm seems highly successful through a comprehensive optimization process of operational parameters.

“Prevention is better than cure”, true statement which all of us neglect. One of the most reasons which cause speedy recovery from any diseases is to discover it in advanced stages. From here come the importance of computer systems which preserve time and achieve accurate results in knowing the diseases and its first symptoms .One of these systems is retinal image analysis system which considered as a key role and the first step of Computer Aided Diagnosis Systems (CAD). In addition to monitor the patient health status under different treatment methods to ensure How it effects on the disease.. In this chapter the authors examine most of approaches that are used for vessel segmentation for retinal images, and a review of techniques is presented comparing between their quality and accessibility, analyzing and catgrizing them. This chapter gives a description and highlights the key points and the performance measures of each one.

In this paper, a region-based image fusion approach were proposed based on the stationary wavelet transform (SWT) in conjunction with marker-controlled watershed segmentation technique. The SWT is redundant, linear and shift invariant and these properties allow SWT to be realized exploiting a recursive algorithm and gives a better approximation than the DWT. The performance of the fusion approach is illustrated via experimental results obtained with a broad series of images and the experimental results used the MODIS multi-spectral bands and Spot panchromatic band to validate the proposed image fusion technique. Moreover, the visual presentation and different evaluation criteria including the standard deviation, the entropy information, the correlation coefficient, the root mean square error, the peak signal to noise ratio and the structural similarity index was used to evaluate the obtained results. The proposed approach achieves superior results compared with the existing work.

Accurate segmentation of retinal blood vessels is an important task in computer aided diagnosis and surgery planning of retinopathy. Despite the high resolution of photographs in fundus photography, the contrast between the blood vessels and retinal background tends to be poor. Furthermore, pathological changes of the retinal vessel tree can be observed in a variety of diseases such as diabetes and glaucoma. Vessels with small diameters are much liable to effects of diseases and imaging problems. In this paper, an automated retinal blood vessels segmentation approach based on two levels optimization principles is proposed. The proposed approach makes use of the artificial bee colony optimization in conjunction with fuzzy cluster compactness fitness function with partial belongness in the first level to find coarse vessels. The dependency on the vessel reflectance is problematic as the confusion with background and vessel distortions especially for thin vessels, so we made use of a second level of optimization. In the second level of optimization, pattern search is further used to enhance the segmentation results using shape description as a complementary feature. Thinness ratio is used as a fitness function for the pattern search optimization. The pattern search is a powerful tool for local search while artificial bee colony is a global search with high convergence speed. The proposed retinal blood vessels segmentation approach is tested on two publicly available databases DRIVE and STARE of retinal images. The results demonstrate that the performance of the proposed approach is comparable with state of the art techniques in terms of sensitivity, specificity and accuracy.

Diabetic retinopathy is a disease, which forms a severe threat on sight. It may reach to blindness among working age people. By analyzing and detecting of vasculature structures in retinal images, we can early detect the diabetes in advanced stages by comparison of its states of retinal blood vessels. In this paper, we present blood vessel segmentation approach, which can be used in computer based retinal image analysis to extract the retinal image vessels. Mathematical morphology and K-means clustering are used to segment the vessels. To enhance the blood vessels and suppress the background information, we perform smoothing operation on the retinal image using mathematical morphology. Then the enhanced image is segmented using K-means clustering algorithm. The proposed approach is tested on the DRIVE dataset and is compared with alternative approaches. Experimental results obtained by the proposed approach showed that it is effective as it achieved average accuracy of 95.10% and best accuracy of 96.25%.

Well maintained road network is an essential requirement for the safety and consistency of vehicles moving on that road and the wellbeing of people in those vehicles. On the other hand, guaranteeing an adequate maintenance by road managers can be achieved via having sufficient and accurate information concerning road infrastructure quality that can be as well utilized concurrently by the widespread means of users’ mobile devices both locally and worldwide. This article proposes a road condition monitoring framework that detects the road anomalies such as speed bumps. In the proposed approach, the main indicator for road anomalies is the gyroscope around gravity rotation in addition to the accelerometer sensor as a cross-validation method to confirm the detection results that were gathered from the gyroscope.

This paper proposes a new approach of 3D watermarking by ensuring the optimal preservation of mesh surfaces. The minimal surface distortion is enforced during watermark embedding stage using Genetic Algorithm (GA) optimization. The watermark embedding is performed only on set of selected vertices come out from k-means clustering technique. These vertices are used as candidates for watermark carriers that will hold watermark bits stream. A 3D surface preservation function is defined according to the distance of a vertex displaced by watermarking to the original surface. A study of the proposed methodology has high robustness against the common mesh attacks while preserving the original object surface during watermarking.

This paper presents an automated retinal blood vessels segmentation
approach based on flower pollination search algorithm (FPSA). The flower pollination
search is a new algorithm based on the flower pollination process of flowering
plants. The FPSA searches for the optimal clustering of the given retinal
image into compact clusters under some constrains. Shape features are used to
further enhance the clustering results using local search method. The proposed
retinal blood vessels approach is tested on a publicly available databases DRIVE
a of retinal images. The results demonstrate that the performance of the proposed
approach is comparable with state of the art techniques in terms of accuracy, sensitivity
and specificity.

This paper proposes a remote sensing image fusion approach
based on a modied version of Brovey transform and wavelets. The aim is
to reduce the spectral distortion in the Brovey transform and spatial dis-
tortion in the wavelets transform. The remote sensing data sets has been
chosen for the image fusion process and the data sets were selected from
dierent satellite images in south western Sinai, Egypt. Experiments were
conducted on a variety of images, and the results of the proposed image
fusion approach were compared with principle component analysis and
the traditional Brovey approach. The obtained results show that the
proposed approach achieves less de
ection and reduces the distortion.
Several quality evaluation metrics were used for the proposed image fu-
sion like standard deviation, correlation coecient, entropy information,
peak signal to noise ratio, root mean square error and structural simi-
larity index. Experimental results obtained from proposed image fusion
approach prove that the use of the Brovey with wavelets can eciently
preserve the spectral information while improving the spatial resolution
of the remote sensing.