A investigação na área da segmentação de tumores cerebrais está ainda em curso, com vários grupos a tentar resolver problemas que estão ainda associados ao tema. Um destes problemas tem a ver com a classificação automática dos tumores em alto e baixo grau, o que permitiria, porventura, facilitar o diagnóstico dos mesmos.
O método desenvolvido no âmbito desta dissertação tem ainda algum espaço a melhorias. Uma das principais tem a ver com a otimização das diferentes etapas, sobretudo na extração das características, correspondente ao maior tempo de processamento. A definição de outras características significativas, nomeadamente de textura, pode ser importante na distinção das diferentes classes. Outra sugestão seria o estudo mais completo do efeito de cada parâmetro do algoritmo, o que poderia melhorar os resultados.
O pré-processamento é uma etapa bastante importante de todo o processo, e seria positivo o estudo de outras técnicas de normalização de intensidade e do seu efeito na qualidade da segmentação. Outro passo de pré-processamento que poderia ser incluído é a remoção do ruido.
Por fim, uma importante etapa que poderia ser implementada posteriormente à floresta de decisão é a regularização espacial. Esta poderia utilizar as probabilidades posteriores da floresta, para potenciar as relações entre vóxeis vizinhos.
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