Divergências de Bregman aplicadas na recuperação de imagens por conteúdo em displasias orais

• Ano de defesa: 2024
• Autor(a) principal: Soares, Tiago Rosa Marques
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Ciência da Computação
• Palavras-chave em Português: Kullback-Leibler; Divergência de Bregman; Displasia; Processamento Digital de Imagem; Recuperação de Imagens; Bregman Divergence; Dysplasia; Digital Image Processing; Image Retrieval; CNPQ::CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA::ESTATISTICA::ANALISE DE DADOS; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAO::LINGUAGENS DE PROGRAMACAO; CNPQ::CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA::ESTATISTICA::FUNDAMENTOS DA ESTATISTICA; Computação; Diagnóstico por imagem; Dentinogênese imperfeita; Imagens digitais; ODS::ODS 3. Saúde e bem-estar - Assegurar uma vida saudável e promover o bem-estar para todos, em todas as idades.
• Link de acesso: https://repositorio.ufu.br/handle/123456789/43304; http://doi.org/10.14393/ufu.di.2024.5124

CBIR systems aim perform image retrieval based on their visual content, using algorithms that describe visual characteristics in the form of numerical features. These features allow finding and displaying similar images, seeking to replicate or surpass human judgment about similarity. The challenge of dealing with unstructured data in large sets of images creates opportunities to develop techniques that identify similarities or dissimilarities between images, depending on the comparison method applied. Most of the work dedicated to the development of structures and algorithms that apply similarity measures uses metric spaces, because metric measures satisfy the basic properties of the metric space. For cases that do not follow these properties, using non-metric similarity measures contributes to the solution of problems in more complex types of data. Bregman Divergence allows the development of effective and applicable methods that use a set of inequality functions, instead of relying on a single objective. This can be successful when metric features do not match human judgment. Obtaining superior results lies in the ability to choose the measure that best fits the problem in question, being possible to use generalizations such as: Mahalanobis and Kullback-Leibler (KL). In this study, the objective is to investigate the similarity or dissimilarity of images related to oral cavity cancer, where features were extracted from slides containing experimentally induced lesions in the tongue of mice of the C57Bl/6 strain, using the carcinogen 4NQO. The use of medical images and image retrieval plays a crucial role in the diagnosis of dysplasias, especially in fields such as radiology, pathology and medicine. For the study, a comparison between metric distances and Bregman Divergences (KL and Mahalanobis) was performed, using vectors of morphological and non-morphological features. The results of the CBIR system using different distance functions were evaluated by the measures of Precision and Recall. Through the results, Bregman Divergences, especially KL, demonstrate better performance compared to metric distances in most of the tested cases.