Brain Tumor Segmentation and Volume Estimation from T1-Contrasted and T2 MRIs
A. Hanuman, and K. Sooknanan.
International Journal of Image Processing (IJIP) 12 (2): 48-62 (June 2018)

Amid the variations of the cancer disease, brain tumors account for the majority deaths among young people. To diagnose and treat this deadly disease effectively, analysis of hundreds of medical images such as Magnetic Resonance Imaging (MRI) scans is usually performed. However, the analyses of these scans are still mainly performed manually, making the procedure not only very tedious and time-consuming for doctors, but also error prone and non-repeatable. Attempts have been made to automate this procedure by performing image processing techniques such as thresholding, region-growing, unsupervised learning (e.g. k-means, fuzzy c-means clustering), and supervised learning (e.g. support vector machines). Some require human interaction. The techniques may be applied on one or more MRI sequence scans. Unfortunately, these automated attempts still result in a high level of error, and more computationally complex algorithms do not guarantee an increase in accuracy. This paper presents a novel, fully automatic brain tumor segmentation and volume estimation method using simple techniques on T1-contrasted and T2 MRIs. This new approach implemented five main steps: preprocessing using anisotropic diffusion, segmentation of tumor regions using k-means clustering, region combination using logical and Morphological operations, error checking using temporal smoothing, and volumetric measurement. When compared with five state-of-the-art algorithms, the proposed algorithm outperformed those in past works. Advances were seen by its noise reduction, increase in accuracy and closeness to actual tumor volume.
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