Localized active contour model with background intensity compensation applied on automatic MR brain tumor segmentation

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• 作者：Elisee Ilunga-Mbuyamba^a ; ^{e.ilungambuyamba@ugto.mx}Author Vitae ; Juan Gabriel Avina&ndash ; Cervantes^a ; ^{avina@ugto.mx}Author Vitae ; Arturo Garcia&ndash ; Perez^a ; ^{arturo@ugto.mx}Author Vitae ; Rene de Jesus Romero&ndash ; Troncoso^a ; ^{troncoso@ugto.mx}Author Vitae ; Hugo Aguirre&ndash ; Ramos^a ; ^{h.aguirreramos@ugto.mx}Author Vitae ; Ivan Cruz&ndash ; Aceves^b ; ^{ivan.cruz@cimat.mx}Author Vitae ; Claire Chalopin^c ; ^{claire.chalopin@medizin.uni-leipzig.de}Author Vitae
• 关键词：00-01 ; 99-00
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：12 January 2017
• 年：2017
• 卷：220
• 期：Complete
• 页码：84-97
• 全文大小：2720 K
• 卷排序：220

文摘

This paper presents a Localized Active Contour Model (LACM) integrating an additional step of background intensity compensation. The region-based active contour models that use statistical intensity information are more sensitive to the high mean intensity distance between consecutive regions. In Magnetic Resonance Imaging (MRI) this distance is great between the foreground and the background, hence it leads to an incorrect delineation of the target. In order to resolve this problem, an automatic process is introduced in our model for balancing the mean intensity distance between an image foreground and its background. The aim is to minimize the attraction effect of the active contour model to the undesired borderlines defined by these two mentioned image regions. By using this approach not only the obtained accuracy outperforms the traditional localized mean separation active contour model, but also it reduces the computation time of the segmentation task. In addition, this method was efficiently applied on automatic brain tumor segmentation in multimodal MRI data. The Hierarchical Centroid Shape Descriptor (HCSD) was used for detecting the region of interest i.e. abnormal tissue so as to automatically initialize the active contour. The validation of experiments was carried out on synthetic images and the quantitative evaluation was performed on the BRATS2012 database. Finally, the accuracy achieved by the proposed method was compared to the localized mean separation intensity, the localized Chan-Vese, the local Gaussian distribution fitting and the local binary fitting models by using the Dice coefficient, Sensitivity, Specificity and the Hausdorff distance. The computation time of the methods was also measured for comparison purposes. The obtained results show that the proposed model outperforms the accuracy of the selected state of the art methods. Moreover, it is also faster than the comparative methods in the medical image segmentation task.