title: Morphological classification of odontogenic keratocysts using Bouligand-Minkowski fractal descriptors.
abstract: The Odontogenic keratocyst (OKC) is a cystic lesion of the jaws, which has high growth and recurrence rates compared to other cysts of the jaws (for instance, radicular cyst, which is the most common jaw cyst type). For this reason OKCs are considered by some to be benign neoplasms. There exist two sub-types of OKCs (sporadic and syndromic) and the ability to discriminate between these sub-types, as well as other jaw cysts, is an important task in terms of disease diagnosis and prognosis. With the development of digital pathology, computational algorithms have become central to addressing this type of problem. Considering that only basic feature-based methods have been investigated in this problem before, we propose to use a different approach (the Bouligand-Minkowski descriptors) to assess the success rates achieved on the classification of a database of histological images of the epithelial lining of these cysts. This does not require the level of abstraction necessary to extract histologically-relevant features and therefore has the potential of being more robust than previous approaches. The descriptors were obtained by mapping pixel intensities into a three dimensional cloud of points in discrete space and applying morphological dilations with spheres of increasing radii. The descriptors were computed from the volume of the dilated set and submitted to a machine learning algorithm to classify the samples into diagnostic groups. This approach was capable of discriminating between OKCs and radicular cysts in 98% of images (100% of cases) and between the two sub-types of OKCs in 68% of images (71% of cases). These results improve over previously reported classification rates reported elsewhere and suggest that Bouligand-Minkowski descriptors are useful features to be used in histopathological images of these cysts.
authors.affiliation: Scientific Computing Group, São Carlos Institute of Physics, University of São Paulo, PO Box 369, 13560-970, São Carlos, SP, Brazil; Institute of Mathematics, Statistics and Scientific Computing, University of Campinas, Rua Sérgio Buarque de Holanda, 651, Cidade Universitária "Zeferino Vaz", Distr. Barão Geraldo, CEP 13083-859 Campinas, SP, Brazil. Electronic address: jbflorindo@ime.unicamp.br.
authors.forename: Joao B
authors.initials: JB
authors.lastname: Florindo
authors.affiliation: Scientific Computing Group, São Carlos Institute of Physics, University of São Paulo, PO Box 369, 13560-970, São Carlos, SP, Brazil. Electronic address: bruno@ifsc.usp.br.
authors.forename: Odemir M
authors.initials: OM
authors.lastname: Bruno
authors.affiliation: Oral Pathology Unit, School of Dentistry, University of Birmingham, 5 Mill Pool way, Edgbaston, Birmingham, B5 7EG United Kingdom. Electronic address: G.Landini@bham.ac.uk.
authors.forename: Gabriel
authors.initials: G
authors.lastname: Landini
mesh_terms: D017709:Fractals; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008853:Microscopy / Q000379:methods*; D009807:Odontogenic Cysts / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Computer-aided detection and diagnosis; Microscopy; Odontogenic cyst; Pattern recognition and classification