Conference Papers

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    Efficient fuzzy clustering based approach to brain tumor segmentation on MR images
    (2011) Arakeri, M.P.; Guddeti, G.
    Image segmentation is one of the most vital and significant step in medical applications. The conventional fuzzy c-means (FCM) clustering is the most widely used unsupervised clustering method for brain tumor segmentation on magnetic resonance (MR) images. However, the major limitation of the conventional FCM is its huge computational time and it is sensitive to initial cluster centers. In this paper, we present a novel efficient FCM algorithm to eliminate the drawback of conventional FCM. The proposed algorithm is formulated by incorporating distribution of the gray level information in the image and a new objective function which ensures better stability and compactness of clusters. Experiments are conducted on brain MR images to investigate the effectiveness of the proposed method in segmenting brain tumor. The conventional FCM and the proposed method are compared to explore the efficiency and accuracy of the proposed method. © 2011 Springer-Verlag.
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    3D AttU-NET for Brain Tumor Segmentation with a Novel Loss Function
    (Institute of Electrical and Electronics Engineers Inc., 2023) Roy, R.; Annappa, B.; Dodia, S.
    In the United States of America (USA), every year 150,000 patients are registered with a secondary brain tumor that is not generated in the brain. This necessitates the need for early brain tumor detection, which in turn will help patients to live longer. For clinical evaluation and treatment, precise segmentation of brain tumors in MRI images is required. This process can be aided by machine learning and efficient image processing, but manual imaging can be time-consuming. In this study, we aim to develop an 3D automated segmentation algorithm with a novel loss function. A 3D attention UNET CNN model was trained using the novel loss function, which was calculated by taking the weighted average of dice loss and focal loss to overcome the class imbalance. Results show the enhancement in the segmentation performance of attention UNET model with an average increase of 5% in the Dice coefficient for all three classes. However, the model's performance was not as strong for enhanced and core tumors. Further research may be needed to optimize performance in these areas. . © 2023 IEEE.