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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Morphological classification of odontogenic keratocysts using Bouligand-Minkowski fractal descriptors

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Florindo, Joao B. ; Bruno, Odemir M. ; Landini, Gabriel
Total Authors: 3
Document type: Journal article
Source: COMPUTERS IN BIOLOGY AND MEDICINE; v. 81, p. 1-10, FEB 1 2017.
Web of Science Citations: 2

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 stiggest that Bouligand Minkowski descriptors are useful features to be used in histopathological images of these cysts. (AU)

FAPESP's process: 13/22205-3 - Tissue spatial organization and fractal analyses for automated plant taxonomy
Grantee:Joao Batista Florindo
Support type: Scholarships abroad - Research Internship - Post-doctor
FAPESP's process: 14/08026-1 - Artificial vision and pattern recognition applied to vegetal plasticity
Grantee:Odemir Martinez Bruno
Support type: Regular Research Grants
FAPESP's process: 12/19143-3 - Fractal Geometry and Image Analysis Applied to Vegetal Biology
Grantee:Joao Batista Florindo
Support type: Scholarships in Brazil - Post-Doctorate