Amr R. Abdel-Dayem

Carotid artery ultrasound image segmentation using fuzzy region growing

In this paper, we propose a new scheme for extracting the contour of the carotid artery using ultrasound images. Starting from a user defined seed point within the artery, the scheme uses the fuzzy region growing algorithm to create a fuzzy connectedness map for the image. Then, the fuzzy connectedness map is thresholded using a threshold selection mechanism to segment the area inside the artery. Experimental results demonstrated the efficiency of the proposed scheme in segmenting carotid artery ultrasound images, and it is insensitive to the seed point location, as long as it is located inside the artery.

A novel morphological-based carotid artery contour extraction

Ultrasound imaging provides an inexpensive tool for monitoring the blood flow within the carotid artery. The precipitation of plaque on the wall of the carotid artery has a great influence on the blood flow within the artery. Due to the poor quality and the presence of noise in ultrasound images, manual extraction of carotid artery walls is a tedious and time-consuming task. In this paper, a novel carotid artery contour extraction is proposed. The proposed scheme consists of four major stages. These stages are: pre-processing stage, quantization stage, morphological contour detection stage, and finally a contour enhancement stage. Experimental results over a set of sample images showed that the proposed scheme produces accurate contours. Thus the proposed scheme can be used as an effective tool in monitoring and detecting plaque precipitation on the walls of the carotid artery.

Watershed segmentation for carotid artery ultrasound images

Summary form only given. This paper introduces a novel segmentation scheme for carotid artery ultrasound images. The proposed scheme is based on watershed segmentation algorithm. It consists of four major stages. These stages are preprocessing, watershed segmentation, region merging and finally boundary extraction. The proposed scheme is tested using a set of carotid artery ultrasound images. The experimental results show that the proposed scheme can produce accurate contours.

Fuzzy C-means clustering for segmenting carotid artery ultrasound images

This paper introduces a fully automated segmentation scheme for carotid artery ultrasound images. The proposed scheme is based on fuzzy cmeans clustering. It consists of four major stages. These stages are preprocessing, feature extraction, fuzzy c-means clustering, and finally boundary extraction. Experimental results demonstrated the efficiency of the proposed scheme in segmenting carotid artery ultrasound images.

Fuzzy Entropy Based Detection of Suspicious Masses in Digital Mammogram Images

Mammography is the standard method for screening and detecting breast abnormalities. In this paper, we propose a novel scheme for suspicious lesion detection in digital mammograms. The proposed scheme is based on image thresholding. The optimal threshold is determined by minimizing the fuzzy entropy of the image. Moreover, the paper introduces a new block-based performance criterion to compare between the computer generated and the radiologist segmented images. Experimental results over a set of sample images showed that the proposed scheme produces accurate segmentation results when compared with the manual results produced by radiologists. Hence the proposed scheme can be used as an effective tool in monitoring and detecting suspicious lesions on digital mammogram images

Predicting Alzheimer's disease by classifying 3D-Brain MRI images using SVM and other well-defined classifiers

Alzheimers disease (AD) is the most common form of dementia affecting seniors age 65 and over. When AD is suspected, the diagnosis is usually confirmed with behavioural assessments and cognitive tests, often followed by a brain scan. Advanced medical imaging and pattern recognition techniques are good tools to create a learning database in the first step and to predict the class label of incoming data in order to assess the development of the disease, i.e., the conversion from prodromal stages (mild cognitive impairment) to Alzheimers disease, which is the most critical brain disease for the senior population. Advanced medical imaging such as the volumetric MRI can detect changes in the size of brain regions due to the loss of the brain tissues. Measuring regions that atrophy during the progress of Alzheimers disease can help neurologists in detecting and staging the disease. In the present investigation, we present a pseudo-automatic scheme that reads volumetric MRI, extracts the middle slices of the brain region, performs segmentation in order to detect the region of brains ventricle, generates a feature vector that characterizes this region, creates an SQL database that contains the generated data, and finally classifies the images based on the extracted features. For our results, we have used the MRI data sets from the Alzheimers Disease Neuroimaging Initiative (ADNI) database.

Detection of arterial lumen in sonographic images based on active contours and diffusion filters

This paper presents a scheme for extracting carotid artery contours from ultrasound images using a modified active contour model. The scheme uses a single seed point as an input. A complex diffusion filter is used to provide a robust estimation of the images edge map. This edge map is used to define the external energy function for the proposed active contour. The scheme produces accurate results compared to the gold standard images. Moreover, the proposed snake model was compared to two snake models found in literature. While the first model uses Canny edge detector, the second employs the Sobel operator to calculate the images edge map. Experimental results over a set of 40 images show that the proposed model outperforms the other two models. Finally, sensitivity analysis over the entire set of test images revealed that the scheme is insensitive to the seed point location, as long as it is located inside the artery area.

Diffusion-Based Detection of Carotid Artery Lumen from Ultrasound Images

This paper presents an experimental study on the effect of using diffusion-based filters on segmenting carotid artery ultrasound images. Moreover, comparisons with other segmentation schemes, found in literature, were conducted. In this study, the segmentation process starts with the original ultrasound image as the initial image u o (the image at time t=0 ). Then, the image diffuses as the time t advances until a steady state is reached. At steady state, the real component of the diffused image will be a smoothed version of the input image, whereas the imaginary component will approximate a smoothed second derivative, which is used to extract the artery contours. The experimental results demonstrated the efficiency of diffusion-based filters in segmenting carotid artery ultrasound images.

Image retargeting using non-uniform scaling with adaptive local search window

This paper presents a new content-aware image-retargeting scheme, based on non-uniform scaling, to adaptively adjust the images dimensions for various screen sizes. Based on an importance map, the energy contribution for each line in the reduced dimension to the overall energy within the image is computed. Then, the image is adaptively mapped and resampled based on the energy contribution function. Experimental results showed that the performance of the proposed scheme is comparable to seam carving in visual quality. However, it is computationally less expensive.

Implementation of Segmentation Methods for the Diagnosis and Prognosis of Mild Cognitive Impairment and Alzheimer Disease

Alzheimers disease (AD) is the most common form of dementia affecting seniors age 65 and over. When AD is suspected, the diagnosis is usually confirmed with behavioural assessments and cognitive tests, often followed by a brain scan. Advanced medical imaging is a good tool to predict conversion from prodromal stages (mild cognitive impairment) to Alzheimers disease. Since volumetric MRI can detect changes in the size of brain regions, measuring those regions that atrophy during the progress of Alzheimers disease can help the neurologist in his diagnostic. In the present investigation, we present an automatic tool that reads volumetric MRI and performs 2-dimensional (volume slices) and volumetric segmentation methods in order to segment gray matter, white matter and cerebrospinal fluid (CSF). We used the MRI data sets database from the Open Access Series of Imaging Studies (OASIS).