Aboul Ella Hassanien (Abo)

This paper introduces a new computer-aided classification system for detection of prostate cancer in
Transrectal Ultrasound images (TRUS). To increase the efficiency of the computer aided classification
process, an intensity adjustment process is applied first, based on the Pulse Coupled Neural Network
(PCNN) with a median filter. This is followed by applying a PCNN-based segmentation algorithm to
detect the boundary of the prostate image. Combining the adjustment and segmentation enable to eliminate PCNN sensitivity to the setting of the various PCNN parameters whose optimal selection can be difficult and can vary even for the same problem. Then, wavelet based features have been extracted and
normalized, followed by application of a rough set analysis to discover the dependency between the
attributes and to generate a set of reduct that contains a minimal number of attributes. Finally, a rough
confusion matrix is designed that contain information about actual and predicted classifications done by a
classification system. Experimental results show that the introduced system is very successful and has high detection accuracy

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.

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.

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%.

This paper presents a feature-based retinal image registration schema. A structural feature, namely, bifurcation structure, has been used for the proposed feature-based registration schema. The bifurcation structure is composed of a master bifurcation point and its three connected neighbors. The characteristic vector of each bifurcation structure consists of the normalized branching angle and length, which is invariant against translation, rotation, scaling, and even modest distortion. The proposed schema is composed of five fundamental phases, namely, input retinal images pre-processing, vascular network detection, noise removal, bifurcation points detection in vascular networks, and bifurcation points matching in pairs of retinal images. The effectiveness of the proposed schema is demonstrated by the experiments with 12 pairs retinal images collected from clinical patients. The registration is carried out through optimizing a certain similarity function, namely, normalized correlation of images. It has been observed that the proposed schema has achieved good performance accuracy.

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.

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.