Anais do IHC'2001 - Departamento de Informática e Estatística - UFSC

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296 Anais do IHC’2001 - IV Workshop sobre Fatores Humanos em Sistemas Computacionais Outra vantagem da saída gráfica seria a fácil detecção e correção de erros no algoritmo, ou seja, ao contrário do aluno ter que utilizar o tradicional método correção – compilação – execução, a maioria dos comandos do Logo3D corresponde a imagens facilmente identificáveis. O uso do software como uma ferramenta de ensino ajuda ao individualizar a aprendizagem e agregar estímulos visuais. Especificamente com o uso da linguagem LOGO, é possível a utilização de uma interface para a construção de conhecimento baseada em desafios cognitivos e na preparação e execução de projetos que tenham relação com o interesse do usuário. Baseado não na repetição, mas no encontro de soluções para desafios cognitivos, a forma de apresentação dos dados no LOGO3D (visualização) auxiliará na construção das noções espaciais, tais como as de profundidade, largura e comprimento. Este elemento é o grande diferencial desta versão do LOGO das outras ditas somente para crianças. Assim, será possível elaborar projetos educacionais tridimensionais e ampliar os desafios para os usuários do LOGO. Referências [1] Galembeck, Frederico C. M., “LOGO”, São Paulo, Atlas – 1987. [2] Malfatti, Silvano e Santos, Selan R., “Implementação do Software Educacional Logo3D – Uma Extensão Tridimensional do LOGO”, Jornada Acadêmica Integrada, Universidade Federal de Santa Maria, outubro de 2000. [3] Papert, S., “LOGO: Computadores e Educação”, 3ª edição, Editora Brasiliense, São Paulo, 1985.
Anais do IHC’2001 - IV Workshop sobre Fatores Humanos em Sistemas Computacionais 297 Structuring the Space of Interaction Practices in the Segmentation of Medical Images: a First Discussion Sílvia D. Olabarriaga and Marcelo S. Pimenta Instituto de Informática – Universidade Federal do Rio Grande do Sul (UFRGS) Caixa Postal 15.064 – 91.501-970 – Porto Alegre – RS – Brazil {silvia,mpimenta}@inf.ufrgs.Br Abstract. This paper presents a first discussion on how to structure the space of interaction practices in the segmentation of medical images. We first present an example of application to set a scenario for discussion. Next we summarize some of the interactive aspects related to image segmentation methods. Finally we present some initial ideas about how to improve the design of ISMs with HCI methodology. 1. Introduction Segmentation is a step in the analysis of digital images where the object of interest is isolated from the background [Gonzalez and Woods (1993)]. Segmentation supports several analysis tasks such as quantification, inspection and retrieval; in all cases, automatic processing is desirable, but sometimes unattainable due to limitations imposed by image acquisition, abnormalities in the scene, or both. Interactive segmentation methods (ISMs) are adopted in applications with a high demand for accurate results, when the volume of images is reasonable, and when the image dimensionality admits human manipulation. As a general rule, the goal of ISMs is to combine a human operator and a computer (also named computational part) to obtain an accurate delineation of the object of interest in an efficient manner. The underlying assumption is that the user knows the correct delineation and, as such, he/she could guide the computational part to determine it efficiently. This is the case of many applications, in particular in the medical field, where clinical practice and research impose several demands on reliability of results obtained with or by computational tools. In this short paper we start a discussion on how to structure the space of interaction practices in the segmentation of medical images. We first present an example of application to set a scenario for discussion. Next we present a review of aspects related to interactive segmentation methods. Finally we present some initial ideas about how to improve the design of ISMs with HCI methodology. 2. A simple example of interactive segmentation in medical imaging As an example of interactive segmentation procedures, consider the following scenario: a pregnant woman is submitted to ultrasound (US) to check the baby's development, which is roughly assessed by the diameter of the baby's head. The doctor manipulates the US probe on the belly, trying to get the best view of the head on the screen, such that its diameter can be most reliably estimated. The image is “frozen” when the doctor is satisfied and then
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