Ali M. Hasan

Spectroscopic, Physical and Topography of Photochemical Process of PVC Films in the Presence of Schiff Base Metal Complexes

The photostability of poly(vinyl chloride), PVC, containing various Schiff base metal complexes (0.5% by weight) was investigated. Various indices corresponding to a number of functional groups were monitored with irradiation of polymeric films to determine their photostabilization activities. The quantum yield of the chain scission (Φcs) of modified polymeric films was found to be (1.15–4.65) × 106. The surface morphology of a PVC sample was investigated by the use of atomic force microscope (AFM). The photostability of PVC films in the presence of Schiff base additives was found to follow the following order: PVC < PVC + CuL2 < PVC + CdL2 < PVC + ZnL2 < PVC + SnL2 < PVC + NiL2. Various mechanisms for PVC films photostability containing the Schiff base additives have been suggested.

Photostabilization of poly(vinyl chloride) – Still on the run

Abstract Polymer science is, of course, driven by the desire to produce new materials for new applications. The success of materials such as polyethylene, polypropylene, poly(vinyl chloride) and polystyrene is such that these materials are manufactured on a huge scale and are indeed ubiquitous.

Automated screening of MRI brain scanning using grey level statistics

Automated algorithm for detecting normality or abnormality in MRI brain scans.A new Modified Grey level Co-occurrence Matrix (MGLCM) method is presented to extract second order statistical texture features for discriminating brain abnormality.MGLCM generates efficient texture feature that is used for measuring the symmetry of MRI brain scan than the tradition GLCM.An accuracy of 97.8% was achieved using MGLCM with 9 orientations and 1 distance. The paper describes the development of an algorithm for detecting and classifying MRI brain slices into normal and abnormal. The proposed technique relies on the prior-knowledge that the two hemispheres of a healthy brain have approximately a bilateral symmetry. We use the modified grey level co-occurrence matrix method to analyze and measure asymmetry between the two brain hemispheres. 21 co-occurrence statistics are used to discriminate the images. The experimental results demonstrate the efficacy of our proposed algorithm in detecting brain abnormalities with high accuracy and low computational time. The dataset used in the experiment comprises 165 patients with 88 having different brain abnormalities whilst the remaining do not exhibit any detectable pathology. The algorithm was tested using a ten-fold cross-validation technique with 10 repetitions to avoid the result depending on the sample order. The maximum accuracy achieved for the brain tumors detection was 97.8% using a Multi-Layer Perceptron Neural Network. Display Omitted

Segmentation of Brain Tumors in MRI Images Using Three-Dimensional Active Contour without Edge

Brain tumor segmentation in magnetic resonance imaging (MRI) is considered a complex procedure because of the variability of tumor shapes and the complexity of determining the tumor location, size, and texture. Manual tumor segmentation is a time-consuming task highly prone to human error. Hence, this study proposes an automated method that can identify tumor slices and segment the tumor across all image slices in volumetric MRI brain scans. First, a set of algorithms in the pre-processing stage is used to clean and standardize the collected data. A modified gray-level co-occurrence matrix and Analysis of Variance (ANOVA) are employed for feature extraction and feature selection, respectively. A multi-layer perceptron neural network is adopted as a classifier, and a bounding 3D-box-based genetic algorithm is used to identify the location of pathological tissues in the MRI slices. Finally, the 3D active contour without edge is applied to segment the brain tumors in volumetric MRI scans. The experimental dataset consists of 165 patient images collected from the MRI Unit of Al-Kadhimiya Teaching Hospital in Iraq. Results of the tumor segmentation achieved an accuracy of 89% ± 4.7% compared with manual processes.

Performance of grey level statistic features versus Gabor wavelet for screening MRI brain tumors: A comparative study

Medical imaging technologies have an important role in the care of all humans organs and disease entities, where they are used widely for the effective diagnosis, treatment and monitoring of the disease. The MRI has been among the most important of all these technologies in the care of patients with brain tumors, where the brain tumor is the one of the most common diseases that cause the death. Screening of brain tumors is an essential to significant improvements in the diagnose and reduce the incidence of death, it can only be as successful as the feature extraction techniques it relies on. Many of these techniques have been used, but it is still not exactly clear which of feature extraction techniques ought to be favored. In this paper, we present here the results of a study in which we compare the proficiency of utilizing grey level statistic method and Gabor wavelet method in detecting and recognizing MRI brain abnormality. The framework that serves as our testbed includes med-sagittal plane detection and correction, feature extraction, feature selection, and lastly classification and comparison.

MRI brain scan classification using novel 3-D statistical features

The paper presents an automated algorithm for detecting and classifying MRI brain slices into normal and abnormal based on a novel three-dimensional modified grey level co-occurrence matrix. This approach is used to analyze and measure asymmetry between the two brain hemispheres. The experimental results demonstrate the efficacy of proposed algorithm in detecting brain abnormalities with high accuracy and low computational time. The dataset used in the experiment comprises 165 patients with 88 having different brain abnormalities whilst the remaining do not exhibit any detectable pathology. The algorithm was tested using a ten-fold cross-validation technique with 10 repetitions to avoid the result depending on the sample order. The maximum accuracy achieved for the brain tumors detection was 93.3% using a Multi-Layer Perceptron Neural Network.

Image Enhancement Based on Fractional Poisson for Segmentation of Skin Lesions Using the Watershed Transform

Image segmentation is considered as a necessary step towards accurate medical analysis by extracting the crucial medical information in identifying abnormalities. This study proposes a new technique for segmentation a malignant melanoma in images. A new filter is proposed for smoothing input images and more accurate segmentation based on fractional Poisson. In the pre-processing step, eight masks of size n × n are created to eliminate noise and obtain a smooth image. The watershed algorithm is used for segmentation with morphological operation to better segment the skin lesion area. The proposed method was capable of improving the accuracy of the segmentation up to 96.47%.

Image Splicing Detection Using Electromagnetism-Like Based Descriptor

This study proposes a simple and powerful descriptor called Electromagnetism-like mechanism descriptor (EMag) for image splicing detection. EMag is based on the electrostatic mechanism that represents the image pixels as electrical charges. For a given tampered image, the EMag algorithm divides an image into blocks and then calculates the final attraction-repulsion force between the central pixel of the square image block and its neighbors. The experimental results using an image splicing dataset provided by Digital Video and Multimedia Lab at Columbia University (DVMM) confirm that EMag impressively outperforms the other widely used descriptors for the detection of image splicing. Support vector machine is used as a classifier that distinguishes between the authentic and spliced images. The experimental results presented demonstrate that the achieved improvements are compatible with other splicing detection methods.

Optical Properties and Morphological Study of New Films Derived From Poly(Vinyl Chloride)-Phenyl Phrine HCl Acid Complexes

The present study focused on modification of the optical properties of PVC films Phenyle Phrine HCl Acid complexes. Poly(vinyl chloride) (PVC) react with Phenyle Phrine HCl Acid (L) in THF to form the PVC‐L compound. PVC‐L has further been reacted with different metals ions to form PVC‐L‐MII complexes. The structure of these complexes has been characterized by FT‐IR and UV‐Vis spectrophotometry. The optical data analyzed and interpreted in terms of the theory of phonon assisted direct electronic transitions. According to energy gap data, the conductivity of PVC and the complexes were obtained. From the microscopic image analysis, the relations between morphology and optical properties were explained.

Image retrieval system based on wavelet network

This paper presents a content-based image retrieval (CBIR) system using the image features extracted by wavelet network and color information descriptors. In this work, average precision and recall are computed for all queries to evaluate the proposed algorithm. The experimental results have demonstrated an improved performance (higher precision and recall values) when compared with other CBIR methods.