Faculty Publications
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Publications by NITK Faculty
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Item 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.Item 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.Item An intelligent content-based image retrieval system for clinical decision support in brain tumor diagnosis(Springer London, 2013) Arakeri, M.P.; Guddeti, G.Accurate diagnosis is crucial for successful treatment of the brain tumor. Accordingly in this paper, we propose an intelligent content-based image retrieval (CBIR) system which retrieves similar pathology bearing magnetic resonance (MR) images of the brain from a medical database to assist the radiologist in the diagnosis of the brain tumor. A single feature vector will not perform well for finding similar images in the medical domain as images within the same disease class differ by severity, density and other such factors. To handle this problem, the proposed CBIR system uses a two-step approach to retrieve similar MR images. The first step classifies the query image as benign or malignant using the features that discriminate the classes. The second step then retrieves the most similar images within the predicted class using the features that distinguish the subclasses. In order to provide faster image retrieval, we propose an indexing method called clustering with principal component analysis (PCA) and KD-tree which groups subclass features into clusters using modified K-means clustering and separately reduces the dimensionality of each cluster using PCA. The reduced feature set is then indexed using a KD-tree. The proposed CBIR system is also made robust against misalignment that occurs during MR image acquisition. Experiments were carried out on a database consisting of 820 MR images of the brain tumor. The experimental results demonstrate the effectiveness of the proposed system and show the viability of clinical application. © 2013, Springer-Verlag London.Item Feature-enhanced deep learning technique with soft attention for MRI-based brain tumor classification(Springer Science and Business Media B.V., 2024) Mohanty, B.C.; Subudhi, P.K.; Dash, R.; Mohanty, B.Brain tumor classification using Magnetic Resonance Imaging (MRI) is a pivotal area in medical diagnostics, with the potential to influence early detection and subsequent treatment strategies. Over the years, various machine learning and deep learning models have been proposed to enhance the accuracy of MRI-based tumor detection. In this evolving landscape, this paper introduces a distinctive deep-learning model that harnesses the power of a soft attention mechanism. We employ a meticulously designed Convolutional Neural Network (CNN) comprising four convolution layers. One of the significant innovations in our approach is the method of feature extraction. Instead of extracting features solely from the final layer, as is common in many models, our approach aggregates and combines features from all layers. This ensures that the vital characteristics intrinsic to each layer are not lost but rather amalgamated into a robust and comprehensive feature vector. The incorporation of a soft attention mechanism at the terminal stages ensures that the most salient and clinically relevant features are emphasized, enhancing classification accuracy. To validate the efficacy of our proposed model, we employed standard datasets for training and testing. A comparative analysis with existing state-of-the-art models affirms the superiority and potential of our approach in the realm of MRI-based brain tumor classification. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.