title: A hybrid approach of using symmetry technique for brain tumor segmentation.
abstract: Tumor and related abnormalities are a major cause of disability and death worldwide. Magnetic resonance imaging (MRI) is a superior modality due to its noninvasiveness and high quality images of both the soft tissues and bones. In this paper we present two hybrid segmentation techniques and their results are compared with well-recognized techniques in this area. The first technique is based on symmetry and we call it a hybrid algorithm using symmetry and active contour (HASA). In HASA, we take refection image, calculate the difference image, and then apply the active contour on the difference image to segment the tumor. To avoid unimportant segmented regions, we improve the results by proposing an enhancement in the form of the second technique, EHASA. In EHASA, we also take reflection of the original image, calculate the difference image, and then change this image into a binary image. This binary image is mapped onto the original image followed by the application of active contouring to segment the tumor region.
authors.affiliation: Department of Computer Science, COMSATS Institute of Information Technology, Abbottabad, Pakistan.
authors.forename: Mubbashar
authors.initials: M
authors.lastname: Saddique
authors.affiliation: Department of Computer Science, COMSATS Institute of Information Technology, Abbottabad, Pakistan.
authors.forename: Jawad Haider
authors.identifier: 0000-0001-9720-6862
authors.initials: JH
authors.lastname: Kazmi
authors.affiliation: Department of Information Science, College of Computing Sciences and Engineering, Kuwait University, Kuwait.
authors.forename: Kalim
authors.identifier: 0000-0001-5202-8601
authors.initials: K
authors.lastname: Qureshi
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012984:Software; D060388:Support Vector Machine; D013997:Time Factors
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