Análise temporal de lesões pulmonares através de textura e forma dinâmicas

• Ano de defesa: 2018
• Autor(a) principal: QUINTANILHA, Darlan Bruno Pontes
• Orientador(a): SILVA, Aristófanes Corrêa
• Banca de defesa: SILVA, Aristófanes Corrêa , PAIVA, Anselmo Cardoso de , NUNES, Rodolfo Acatauassú , LOPES, Hélio Côrtes Vieira , MEDEIROS, Fátima Nelsizeuma Sombra de
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
• Departamento: DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET
• País: Brasil
• Palavras-chave em Português: Imagens médicas; Lesão pulmonar; Detecção de mudança; Análise temporal; Mistura de texturas dinâmicas; Descritores de forma dinâmica.
• Palavras-chave em Inglês: Medical image; Lung lesion; Change detection; Temporal analysis; Dynamic textures mixture; Dynamic shape features.
• Área do conhecimento CNPq: Engenharia de Software
• Link de acesso: https://tedebc.ufma.br/jspui/handle/tede/2292

Lung cancer is still one of the most popular incidents worldwide. Late detection of neoplasia and the proliferation capacity of malignant cells hinder targeted therapies, which often fail. The temporal evaluation is a useful tool to analyze the biological behavior of a lung lesion before, during and after treatment, or lesions of undetermined diagnosis. This paper aims to provide more detailed information about the changes of lesions complementing studies on the biological activity of lung masses and nodules. The methodology presented describes changes in texture and shape of a lung lesion over time. The lesion tissues are segmented using a mixture of dynamic texture model, according to the texture change over time. Texture change descriptors are extracted from each segmented region, while shape change descriptors are extracted from the entire lesion. The study was conducted on two bases of chest-computed tomography: a Public Lung Database, which has lesions that undergo evaluation for drug therapy; and a private base of initially indeterminate diagnosis of lung lesions, but later classified as benign after lesion follow-up. The results of the proposed techniques showed that the variance in Z-score of measures extracted from shape change in the lesions was 0.02 in the private database and 1.47 in the public database, while the variance of measures extracted from texture change was 0.49 in the private database and 1.15 in the public database. A classification model was proposed with the descriptors extracted to predict the diagnosis of the lesion, resulting in an accuracy of 97.2%.