NDLI: Tumor segmentation from single contrast MR images of human brain

Content Provider	IEEE Xplore Digital Library
Author	Hui Tang Huangxiang Lu Weiping Liu Xiaodong Tao
Copyright Year	2015
Description	Author affiliation: Healthcare Philips Res. China, China (Hui Tang; Huangxiang Lu; Weiping Liu; Xiaodong Tao)
Abstract	Detection of brain tumors may help in diagnosis, therapy planning and treatment planning. Brain tumor detection in scout scan may also help in identify the surrounding tissue of the pathology and thus help in imaging parameter optimization. We propose an automatic method to segment brain tumor on one single T2W image. The proposed method for the brain tumor segmentation consists of three steps. In the first step, we normalize the image intensity and register to a standard brain space. Second, we perform a pixel-wise classification using a random forest classification method. For each pixel, 198 features are extracted, including multiscale intensity-based features, multiscale template-based features, multiscale shape based methods, multiscale texture-based features as well as context-aware features. After the pixel-wise classification, we further exclude false positives in a morphological way. Each independent connected objects from the second step, which either has the maximum volume or has a volume over 10 $cm^{3},$ is considered as true tumors. The classifier is trained and evaluated using a leave-one-out framework on a publically available database. Our method successfully detects out tumors in 28 out of 30 dataset (successful rate = 93.3%). The accuracy for the pixel-wise classification is averagely 97.5% for low grade tumors and 96.7% for high grade tumors. The final segmentation is evaluated using Dice Similarity Coefficient, which is 78.8% for low grade tumors and 83.0% for high grade tumors.
Starting Page	46
Ending Page	49
File Size	1307497
Page Count	4
File Format	PDF
ISBN	9781479923748
DOI	10.1109/ISBI.2015.7163813
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tumors Feature extraction Image segmentation Magnetic resonance imaging Biomedical imaging Accuracy T2W MRI Brain Tumor Random Forest
Content Type	Text
Resource Type	Article

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