Brain tumor segmentation with Vander Lugt correlator based active contour. (July 2018) - Essadike, Abdelaziz; Ouabida, Elhoussaine; Bouzid, Abdenbi
Highlights: We present a fast and fully automatic brain tumor detection and segmentation system based on a numerical simulation of the optical Vander Lugt correlator with an active contour model. We considered the design of a specific filter and investigated its impact on the detection of all brain tumor types. We considered several active contour models and investigated their impact on the segmentation task. We use two benchmark databases: BRATS 2012 and 2013 to test the proposed system. To contextualize the results of our proposed method, we investigate several evaluation criteria that are adapted to the tumorous tissue segmentation in the stat-of-the-art. Graphical abstract: Abstract: Background and objective: The manual segmentation of brain tumors from medical images is an error-prone, sensitive, and time-absorbing process. This paper presents an automatic and fast method of brain tumor segmentation. Methods: In the proposed method, a numerical simulation of the optical Vander Lugt correlator is used for automatically detecting the abnormal tissue region. The tumor filter, used in the simulated optical correlation, is tailored to all the brain tumor types and especially to the Glioblastoma, which considered to be the most aggressive cancer. The simulated optical correlation, computed between Magnetic Resonance Images (MRI) and this filter, estimates precisely and automatically the initial contour inside the tumorous tissue. Further, in the segmentation part, the detected initial contour is used to define an active contour model and presenting the problematic as an energy minimization problem. As a result, this initial contour assists the algorithm to evolve an active contour model towards the exact tumor boundaries. Equally important, for a comparison purposes, we considered different active contour models and investigated their impact on the performance of the segmentation task. Several images from BRATS database with tumors anywhere in images and having different sizes, contrast, and shape, are used to test the proposed system. Furthermore, several performance metrics are computed to present an aggregate overview of the proposed method advantages. Results: The proposed method achieves a high accuracy in detecting the tumorous tissue by a parameter returned by the simulated optical correlation. In addition, the proposed method yields better performance compared to the active contour based methods with the averages of Sensitivity=0.9733, Dice coefficient = 0.9663, Hausdroff distance = 2.6540, Specificity = 0.9994, and faster with a computational time average of 0.4119 s per image. Conclusions: Results reported on BRATS database reveal that our proposed system improves over the recently published state-of-the-art methods in brain tumor detection and segmentation.