A Review on Image Segmentation for Brain Tumor Detection

Kulkarni, Sunita M.; Sundari, G.

doi:10.1109/iceca.2018.8474893

Cited by 6 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Brain tumour identification has been the focus of many studies in the past few decades and as a result of the large amount of published work, many reviews are available listing the methods and techniques used. The reviews focused primarily on two approaches: non-AI-based approaches such as [1], [8], [12], [13], [14], [15], [16], [17] and artificial intelligence-based approaches such as [18], [19], [20], [21].…”

Section: Background and Reviewmentioning

confidence: 99%

Symmetry-Based Brain Abnormality Detection Using Machine Learning

Al-Azawi

2022

View full text Add to dashboard Cite

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 and 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.

show abstract

Section: Background and Reviewmentioning

confidence: 99%

Symmetry-Based Brain Abnormality Detection Using Machine Learning

Al-Azawi

2022

View full text Add to dashboard Cite

show abstract

“…Due to the availability of resources and the large amount of published work, many reviews have been published listing the methods and techniques used. The reviews focused primarily on two types of methods: non-AI-based approaches that do not use AI algorithms such as thresholding, image segmentation, and clustering [1], [3], [4], [5], [6], [7], [8], and [9] and artificial intelligence-based approaches that use AI algorithms such as artificial neural networks, fuzzy logic, support vector machine, and deep learning [10], [11], [12], and [13].…”

Section: Introductionmentioning

confidence: 99%

Saliency-Based Brain Abnormality Identification in Magnetic Resonance Images

Al-Azawi¹

2021

View full text Add to dashboard Cite

Due to the great development in the field of digital image processing, it has become an integral and important part of computer-aided diagnosis systems. Magnetic resonance image (MRI) processing is one of the hot fields that attracted researchers due to its role in improving and speeding up diagnosis. This work presents a new method for identifying abnormal regions in brain MR images using saliency recognition techniques. This is because tumour regions or abnormal regions share with saliency the fact that these regions differ from the rest of the image in terms of size, luminance, and texture. In this paper, we use a saliency extraction method that uses the irregularity of the regions in its definition, as this method depends on the basis that the salient areas in the image are rare and different from the rest of the image, which is what we need to determine the tumour area. The proposed algorithm is applied to a standard dataset and the obtained result showed a high level of accuracy, where the precision, recall, F-measure, and accuracy averages reached 91.8%, 96.2%, 84.5%, and 96% respectively. The results were discussed thoroughly, and the limitations were identified and discussed as well.

show abstract