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Automatic three‐dimensional detection and volume estimation of low‐grade gliomas
Author(s) -
Boudrioua Asma,
Aloui Abdelouahab,
Solaiman Basel,
Asli Larbi,
Ben Salem Douraied,
Tliba Souhil
Publication year - 2021
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22557
Subject(s) - computer science , initialization , level set (data structures) , segmentation , volume (thermodynamics) , artificial intelligence , level set method , minimum bounding box , pattern recognition (psychology) , set (abstract data type) , algorithm , image segmentation , image (mathematics) , physics , quantum mechanics , programming language
Brain tumor segmentation is routinely needed in diagnostic radiology. Automatic volume estimation has been used for diagnosis improvement and treatment process. This article presents a new approach for tumor volume estimation, using a variational level set method. The new adapted representation of intensity allows the method to be efficient in the region of interest identification, regardless the shape, size, and location of the tumor. For the estimation of an optimal bounding box, a fuzzy preference optimization model is used. The proposed approach is suitable for the zero level set initialization as well as for the reduction of the processing area, which ultimately speeds up the curve evolution. Moreover, tumor contours are determined using the hybrid level set technique, which combines the gradient and local phase information as an edge indicator term. Such an approach is robust to attenuation and intensity inhomogeneity. The proposed method is evaluated using a set of real and synthetic images. Our method achieved a performance of 96% accuracy, with an average execution time of 4.75 seconds. The proposed method is fast, accurate, and does not require training data or prior knowledge. With such experimental results, our approach outperforms 18 state‐of‐the‐art methods.