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KMID : 1137820190400030105
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의공학회지
2019 Volume.40 No. 3 p.105 ~ p.115
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Development of Brain Tumor Detection using Improved Clustering Method on MRI-compatible Robotic Assisted Surgery
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Kim Dae-Gwan
Cha Kyoung-Rae
Seung Sung-Min
Jeong Se-Mi
Choi Jong-Kyun
Roh Ji-Hyoung
Park Chung-Hwan
Song Tae-Ha
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Abstract
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Brain tumor surgery may be difficult, but it is also incredibly important. The technological improvements for traditional brain tumor surgeries have always been a focus to improve the precision of surgery and release the potential of the technology in this important area of the body. The need for precision during brain tumor surgery has led to an increase in Robotic-assisted surgeries (RAS). One of the challenges to the widespread acceptance of RAS in the neurosurgery is to recognize invisible tumor accurately. Therefore, it is important to detect brain tumor size and location because surgeon tries to remove as much tumor as possible. In this paper, we proposed brain tumor detection procedures for MRI (Magnetic Resonance Imaging) system. A method of automatic brain tumor detection is needed to accurately target the location of the lesion during brain tumor surgery and to report the location and size of the lesion. In the qualitative assessment, the proposed method showed better results than those obtained with other brain tumor detection methods. Comparisons among all assessment criteria indicated that the proposed method was significantly superior to the threshold method with respect to all assessment criteria. The proposed method was effective for detecting brain tumor.
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KEYWORD
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Medical imaging, MRI, K-Means, Robotic-assisted surgery, Image processing, Brain tumor
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