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Article type: Research Article
Authors: Lather, Mansi; * | Singh, Parvinder
Affiliations: Department of Computer Science and Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonepat, India
Correspondence: [*] Corresponding author. Mansi Lather, Department of Computer Science and Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonepat 131039, India. E-mail: [email protected].
Abstract: Due to the complexity of the task involved in extracting and segmenting the tumor area from the images, it is very challenging to be successful in detecting the disorders. This paper presents a method that can handle the various issues related to brain tumor segmentation, such as noise reduction, artifact removal, and visual interpretation. In this paper, an advanced brain tumor segmentation approach is proposed that is working in different phases such as pre-processing that includes image enhancement and noise removal from the input image, Stationary Wavelet Transform (SWT) based feature extraction and Sine Tree-Seed Algorithm (STSA) based modified K-means clustering algorithm for segmentation. In addition to this, the proposed approach is analyzed for its effectiveness by considering the impact of Gaussian and speckle noise on the original image. The experimental results have been evaluated in three different cases of the input noise in terms of accuracy, precision, recall, F-score, and Jaccard. Finally, a comparative analysis is performed with different conventional approaches to prove the effectiveness of the proposed scheme. The result analysis shows an improvement of approximately 1% in terms of accuracy, 4%, and 5% in terms of precision and recall respectively when compared to the other techniques.
Keywords: Image segmentation, medical image processing, image analysis, K-means clustering
DOI: 10.3233/JIFS-212709
Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2579-2595, 2022
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