This study introduces an innovative approach to identifying abnormal regions in brain MRI images ... more This study introduces an innovative approach to identifying abnormal regions in brain MRI images by utilising saliency recognition techniques. Tumour and abnormal regions often diHer from the rest of the image in terms of size, luminance, and texture, making saliency detection a fitting method for identifying such irregularities. The researchers employed a saliency extraction method, focusing on the rarity and distinctiveness of the tumour areas. When tested on a standard dataset, the algorithm achieved impressive performance, with precision, recall, F-measure, and accuracy averaging 91.8%, 96.2%, 84.5%, and 96%, respectively. These results demonstrate the eHectiveness of the method, although the study also recognises and discusses certain limitations [1].
Medical image processing, which includes many applications such as magnetic resonance image (MRI)... more Medical image processing, which includes many applications such as magnetic resonance image (MRI) processing, is one of the most significant fields of computer-aided diagnostic (CAD) systems. the detection and identification of abnormalities in the magnetic resonance imaging of the brain is one of the important applications that uses magnetic resonance imaging and digital image processing techniques. In this study, we present a method that relies on the symmetry and similarity between the two lobes of the brain to determine if there are any abnormalities in the brain because tumours cause deformations in the shape of one of the lobes, which affects this symmetry. The proposed approach overcomes the challenge arising from different shapes of brain images of different people, which poses an obstacle to some approaches that rely on comparing one person's brain image with other people's brain images. In the proposed method the image of the brain is divided into two parts, one for the left lobe and the other for the right lobe. Some measures are extracted from the features of the image of each lobe separately and the distance between the corresponding metrics are calculated. These distances are used as the independent variables of the classification algorithm which determines the class to which the brain belongs. Metrics extracted from various features, such as colour and texture, were studied, discussed and used in the classification process. The proposed algorithm was applied to 366 images from standard datasets and four classifiers were tested namely Naïve Bayes (NB), random forest (RF), logistic regression (LR), and support vector machine (SVM). The obtained results from these classifiers have been discussed thoroughly where it was found that the best results were obtained from RF classifiers where the accuracy was 98.2%. Finally, the results obtained and the limitations were discussed and benchmarked with state-of-the-art approaches.
Medical image processing, which includes many applications such as magnetic resonance image (MRI)... more Medical image processing, which includes many applications such as magnetic resonance image (MRI) processing, is one of the most significant fields of computer-aided diagnostic (CAD) systems. the detection and identification of abnormalities in the magnetic resonance imaging of the brain is one of the important applications that uses magnetic resonance imaging and digital image processing techniques. In this study, we present a method that relies on the symmetry and similarity between the two lobes of the brain to determine if there are any abnormalities in the brain because tumours cause deformations in the shape of one of the lobes, which affects this symmetry. The proposed approach overcomes the challenge arising from different shapes of brain images of different people, which poses an obstacle to some approaches that rely on comparing one person’s brain image with other people's brain images. In the proposed method the image of the brain is divided into two parts, one for th...
In our previous studies, we showed that brain abnormalities can be detected by comparing the feat... more In our previous studies, we showed that brain abnormalities can be detected by comparing the features extracted from the two lobes with each other. Based on this, many metrics, such as those extracted from colour or texture features, have been extracted and used. The large number of extracted metrics posed a challenge in terms of how important each metric is. In this research, we use the mutual information content to measure the importance of the metrics and their influence on the classification process as it gives an indication of how the output and each input are related to each other. The algorithm was applied to 366 images, from which eleven metrics were extracted and studied. Random forest classifier was used as it was proven that it gives the highest accuracy. The obtained results showed that 30% of the features can be eliminated without a significant effect on the accuracy.
Image recognition and understanding is one of the most interesting fields of researches. Its main... more Image recognition and understanding is one of the most interesting fields of researches. Its main idea is to bridge the gap between the high level human image understanding and the low level machine image representation. Quite a lot of applications have been suggested in different fields like medicine, industry, robotics, satellite imagery and other applications. This paper proposes a new approach of traffic signs image recognition and understanding using computational intelligent techniques and the application of this approach on intelligent cars which can recognize the traffic signs and take a decision according to the signs it reads. Supervised machine learning has been selected since the algorithm does not need to classify the images but to identify their precise meaning. Different neural networks have been trained and used in this paper. The best neural network has been selected, which uses genetic algorithms in its training, and is known as evolutionary training neural network...
ELCVIA Electronic Letters on Computer Vision and Image Analysis
Searching for an image in a database is important in different applications; hence, many algorith... more Searching for an image in a database is important in different applications; hence, many algorithms have been proposed to identify the contents of the image. In some applications, but not all, identifying the content of the image as a whole can offer good results. Searching for an object inside the image is more important in most applications than identifying the image as a whole. Therefore, studies focused on segmenting the image into small sub-images and identified their contents. In view of the concepts of human attention, various literature defined saliency as a computer representation of it, where different algorithms were developed to extract the salient regions. These salient regions, which are the regions that attract human attention, are used to identify the most important regions that contain important objects in the image. In this paper, we introduce a new algorithm that utilises the saliency principles to identify the contents of an image and search for similar objects i...
... REFERENCES - Ballord D.and Brown С ,1982. Computer vision, prin-tice-Hall,Inc. - Gonzalez R. ... more ... REFERENCES - Ballord D.and Brown С ,1982. Computer vision, prin-tice-Hall,Inc. - Gonzalez R. and Wiulz P., 1987. Digital image pro-cessing, Addision-Wesley Pup.Con.Inc. - Gircenfcld JS and Schenk AF, 1989. Experiments with edge based stereo Matching, Photag. Eng. ...
BSTRACT An edge matching technique has been used in this work where an algorithm was developed fo... more BSTRACT An edge matching technique has been used in this work where an algorithm was developed for detecting & coding the edge points which depends on the direction of the edge points the coding is followed by edge thining, edge linking & isolated points removing. Then points tracing process is performed to form the straight lines. Lines matching operation is performed to group the lines in corresponding lines pairs. Features that are used in the matching process are, line length, line orientation ends point coordinates & line location.
Machine vision is still a challenging topic and attracts many researchers. One of the significant... more Machine vision is still a challenging topic and attracts many researchers. One of the significant differences between machine vision and human vision is attention whuch is one of the important properties of Human Vision System, with which the human can focus only on part of the scene at a time; scenes with more abrupt features shall attract human attention more than other regions. In this paper, we will simulate the human attention and discuss its application in machine vision and how it improves the result of the retrieval process and image identification and understanding. Artificial intelligence is used to give the algorithm the necessary intelligence to make it closer to human vision system. Its role is to identify and classify the salient points that are obtained from eye trackers or from saliency extraction algorithms.
This paper, presents a new technique in image saliency identification and it application in machi... more This paper, presents a new technique in image saliency identification and it application in machine vision. Saliency identification needs to have a good knowledge about human attention; therefore, the benefit of applying human attention in machine vision and how it improves the performance of machine vision shall be discussed. Salient points identification is one of the important features of human vision system, which we will utilize and discuss in this work. Several algorithms and definitions have been proposed to identify the saliency of a region in an image. The proposed technique utilized the irregularity of the region as a measure of saliency. Edges are used to measure the irregularity of the regions in the image. In addition, we shall study the effect of smoothing on the suggested algorithm.
This study introduces an innovative approach to identifying abnormal regions in brain MRI images ... more This study introduces an innovative approach to identifying abnormal regions in brain MRI images by utilising saliency recognition techniques. Tumour and abnormal regions often diHer from the rest of the image in terms of size, luminance, and texture, making saliency detection a fitting method for identifying such irregularities. The researchers employed a saliency extraction method, focusing on the rarity and distinctiveness of the tumour areas. When tested on a standard dataset, the algorithm achieved impressive performance, with precision, recall, F-measure, and accuracy averaging 91.8%, 96.2%, 84.5%, and 96%, respectively. These results demonstrate the eHectiveness of the method, although the study also recognises and discusses certain limitations [1].
Medical image processing, which includes many applications such as magnetic resonance image (MRI)... more Medical image processing, which includes many applications such as magnetic resonance image (MRI) processing, is one of the most significant fields of computer-aided diagnostic (CAD) systems. the detection and identification of abnormalities in the magnetic resonance imaging of the brain is one of the important applications that uses magnetic resonance imaging and digital image processing techniques. In this study, we present a method that relies on the symmetry and similarity between the two lobes of the brain to determine if there are any abnormalities in the brain because tumours cause deformations in the shape of one of the lobes, which affects this symmetry. The proposed approach overcomes the challenge arising from different shapes of brain images of different people, which poses an obstacle to some approaches that rely on comparing one person's brain image with other people's brain images. In the proposed method the image of the brain is divided into two parts, one for the left lobe and the other for the right lobe. Some measures are extracted from the features of the image of each lobe separately and the distance between the corresponding metrics are calculated. These distances are used as the independent variables of the classification algorithm which determines the class to which the brain belongs. Metrics extracted from various features, such as colour and texture, were studied, discussed and used in the classification process. The proposed algorithm was applied to 366 images from standard datasets and four classifiers were tested namely Naïve Bayes (NB), random forest (RF), logistic regression (LR), and support vector machine (SVM). The obtained results from these classifiers have been discussed thoroughly where it was found that the best results were obtained from RF classifiers where the accuracy was 98.2%. Finally, the results obtained and the limitations were discussed and benchmarked with state-of-the-art approaches.
Medical image processing, which includes many applications such as magnetic resonance image (MRI)... more Medical image processing, which includes many applications such as magnetic resonance image (MRI) processing, is one of the most significant fields of computer-aided diagnostic (CAD) systems. the detection and identification of abnormalities in the magnetic resonance imaging of the brain is one of the important applications that uses magnetic resonance imaging and digital image processing techniques. In this study, we present a method that relies on the symmetry and similarity between the two lobes of the brain to determine if there are any abnormalities in the brain because tumours cause deformations in the shape of one of the lobes, which affects this symmetry. The proposed approach overcomes the challenge arising from different shapes of brain images of different people, which poses an obstacle to some approaches that rely on comparing one person’s brain image with other people's brain images. In the proposed method the image of the brain is divided into two parts, one for th...
In our previous studies, we showed that brain abnormalities can be detected by comparing the feat... more In our previous studies, we showed that brain abnormalities can be detected by comparing the features extracted from the two lobes with each other. Based on this, many metrics, such as those extracted from colour or texture features, have been extracted and used. The large number of extracted metrics posed a challenge in terms of how important each metric is. In this research, we use the mutual information content to measure the importance of the metrics and their influence on the classification process as it gives an indication of how the output and each input are related to each other. The algorithm was applied to 366 images, from which eleven metrics were extracted and studied. Random forest classifier was used as it was proven that it gives the highest accuracy. The obtained results showed that 30% of the features can be eliminated without a significant effect on the accuracy.
Image recognition and understanding is one of the most interesting fields of researches. Its main... more Image recognition and understanding is one of the most interesting fields of researches. Its main idea is to bridge the gap between the high level human image understanding and the low level machine image representation. Quite a lot of applications have been suggested in different fields like medicine, industry, robotics, satellite imagery and other applications. This paper proposes a new approach of traffic signs image recognition and understanding using computational intelligent techniques and the application of this approach on intelligent cars which can recognize the traffic signs and take a decision according to the signs it reads. Supervised machine learning has been selected since the algorithm does not need to classify the images but to identify their precise meaning. Different neural networks have been trained and used in this paper. The best neural network has been selected, which uses genetic algorithms in its training, and is known as evolutionary training neural network...
ELCVIA Electronic Letters on Computer Vision and Image Analysis
Searching for an image in a database is important in different applications; hence, many algorith... more Searching for an image in a database is important in different applications; hence, many algorithms have been proposed to identify the contents of the image. In some applications, but not all, identifying the content of the image as a whole can offer good results. Searching for an object inside the image is more important in most applications than identifying the image as a whole. Therefore, studies focused on segmenting the image into small sub-images and identified their contents. In view of the concepts of human attention, various literature defined saliency as a computer representation of it, where different algorithms were developed to extract the salient regions. These salient regions, which are the regions that attract human attention, are used to identify the most important regions that contain important objects in the image. In this paper, we introduce a new algorithm that utilises the saliency principles to identify the contents of an image and search for similar objects i...
... REFERENCES - Ballord D.and Brown С ,1982. Computer vision, prin-tice-Hall,Inc. - Gonzalez R. ... more ... REFERENCES - Ballord D.and Brown С ,1982. Computer vision, prin-tice-Hall,Inc. - Gonzalez R. and Wiulz P., 1987. Digital image pro-cessing, Addision-Wesley Pup.Con.Inc. - Gircenfcld JS and Schenk AF, 1989. Experiments with edge based stereo Matching, Photag. Eng. ...
BSTRACT An edge matching technique has been used in this work where an algorithm was developed fo... more BSTRACT An edge matching technique has been used in this work where an algorithm was developed for detecting & coding the edge points which depends on the direction of the edge points the coding is followed by edge thining, edge linking & isolated points removing. Then points tracing process is performed to form the straight lines. Lines matching operation is performed to group the lines in corresponding lines pairs. Features that are used in the matching process are, line length, line orientation ends point coordinates & line location.
Machine vision is still a challenging topic and attracts many researchers. One of the significant... more Machine vision is still a challenging topic and attracts many researchers. One of the significant differences between machine vision and human vision is attention whuch is one of the important properties of Human Vision System, with which the human can focus only on part of the scene at a time; scenes with more abrupt features shall attract human attention more than other regions. In this paper, we will simulate the human attention and discuss its application in machine vision and how it improves the result of the retrieval process and image identification and understanding. Artificial intelligence is used to give the algorithm the necessary intelligence to make it closer to human vision system. Its role is to identify and classify the salient points that are obtained from eye trackers or from saliency extraction algorithms.
This paper, presents a new technique in image saliency identification and it application in machi... more This paper, presents a new technique in image saliency identification and it application in machine vision. Saliency identification needs to have a good knowledge about human attention; therefore, the benefit of applying human attention in machine vision and how it improves the performance of machine vision shall be discussed. Salient points identification is one of the important features of human vision system, which we will utilize and discuss in this work. Several algorithms and definitions have been proposed to identify the saliency of a region in an image. The proposed technique utilized the irregularity of the region as a measure of saliency. Edges are used to measure the irregularity of the regions in the image. In addition, we shall study the effect of smoothing on the suggested algorithm.
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Papers by Mohammad Al-Azawi