title: An Effective Occipitomental View Enhancement Based on Adaptive Morphological Texture Analysis.
abstract: This paper aims to present an algorithm that specifically enhances maxillary sinuses using a novel contrast enhancement technique based on the adaptive morphological texture analysis for occipitomental view radiographs. First, the skull X-ray (SXR) is decomposed into rotational blocks (RBs). Second, each RB is rotated into various directions and processed using morphological kernels to obtain the dark and bright features. Third, a gradient-based block segmentation decomposes the interpolated feature maps into feature blocks (FBs). Finally, the histograms of FBs are equalized and overlaid locally to the input SXR. The performance of the proposed method was evaluated on an independent dataset, which comprises of 145 occipitomental view-based human SXR images. According to the experimental results, the proposed method is able to increase the diagnosis accuracy by 83.45% compared with the computed tomography modality as the gold standard.
authors.forename: Peter
authors.initials: P
authors.lastname: Chondro
authors.forename: Hao-Chun
authors.initials: HC
authors.lastname: Hu
authors.forename: Hsuan-Yen
authors.initials: HY
authors.lastname: Hung
authors.forename: Shin-Yuan
authors.initials: SY
authors.lastname: Chang
authors.forename: Lieber Po-Hung
authors.initials: LP
authors.lastname: Li
authors.forename: Shanq-Jang
authors.initials: SJ
authors.lastname: Ruan
mesh_terms: D000465:Algorithms; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008443:Maxillary Sinus / Q000000981:diagnostic imaging*; D011859:Radiography; D012886:Skull / Q000000981:diagnostic imaging*