Automated brain tumor segmentation in magnetic resonance imaging based on sliding-window technique and symmetry analysis.
BACKGROUND
Brain tumor segmentation from magnetic resonance imaging (MRI) is an important step toward surgical planning, treatment planning, monitoring of therapy. However, manual tumor segmentation commonly used in clinic is time-consuming and challenging, and none of the existed automated methods are highly robust, reliable and efficient in clinic application. An accurate and automated tumor segmentation method has been developed for brain tumor segmentation that will provide reproducible and objective results close to manual segmentation results.


METHODS
Based on the symmetry of human brain, we employed sliding-window technique and correlation coefficient to locate the tumor position. At first, the image to be segmented was normalized, rotated, denoised, and bisected. Subsequently, through vertical and horizontal sliding-windows technique in turn, that is, two windows in the left and the right part of brain image moving simultaneously pixel by pixel in two parts of brain image, along with calculating of correlation coefficient of two windows, two windows with minimal correlation coefficient were obtained, and the window with bigger average gray value is the location of tumor and the pixel with biggest gray value is the locating point of tumor. At last, the segmentation threshold was decided by the average gray value of the pixels in the square with center at the locating point and 10 pixels of side length, and threshold segmentation and morphological operations were used to acquire the final tumor region.


RESULTS
The method was evaluated on 3D FSPGR brain MR images of 10 patients. As a result, the average ratio of correct location was 93.4% for 575 slices containing tumor, the average Dice similarity coefficient was 0.77 for one scan, and the average time spent on one scan was 40 seconds.


CONCLUSIONS
An fully automated, simple and efficient segmentation method for brain tumor is proposed and promising for future clinic use. Correlation coefficient is a new and effective feature for tumor location.
Digital Medical Research Center, Shanghai Medical School, Fudan University, Shanghai 200032, China.
Yanyun
Y
Lian
Digital Medical Research Center, Shanghai Medical School, Fudan University, Shanghai 200032, China; Digital Medical Research Center, Shanghai Medical School, Fudan University, Shanghai 200032, China. Email: zjsong@fudan.edu.cn.
Zhijian
Z
Song
D000465:Algorithms; D001932:Brain Neoplasms / Q000175:diagnosis*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*