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Segmentation of osteosarcoma in MRI images by K-means clustering, Chan-Vese segmentation, and iterative Gaussian filtering
Published in John Wiley and Sons Inc
Volume: 15
Issue: 6
Pages: 1310 - 1318

Unlike other types of tumours, automated osteosarcoma segmentation in magnetic resonance images (MRI) is a challenging task due to its different and unique intensity and texture. This paper presents a technique for segmenting osteosarcoma in MRI images using a combination of image processing techniques which include K-means clustering, Chan-Vese segmentation, iterative Gaussian filtering, and Canny edge detection. In addition, the proposed technique involves iterative morphological operations and object counting. The technique was tested using 50 MRI scan images that contain osteosarcoma tumours. The proposed technique was able to segment the osteosarcoma regardless of the variations in their intensities, textures and locations. The performance of the technique was measured by calculating the values for precision, recall, specificity, Dice score coefficient, accuracy and the running time (RT) for all tested cases. The proposed technique achieved 95.96% precision, 86.15% recall, 99.51% specificity, 89.84% Dice score coefficient, 98.02% accuracy, and 191.62 s average running time. This technique can assist clinicians in making treatment plans for patients with osteosarcoma. © 2020 The Authors. IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology

About the journal
JournalData powered by TypesetIET Image Processing
PublisherData powered by TypesetJohn Wiley and Sons Inc
Open AccessNo