Anisotropic Diffusion for Medical Image EnhancementCSCJournals
Advances in digital imaging techniques have made possible the acquisition of large volumes of Transrectal Ultrasound (TRUS) prostate images so that there is considerable demand for automated segmentation. Prostate cancer diagnosis and treatment rely on segmentation of these Transrectal Ultrasound (TRUS) prostate images, a challenging and difficult task due to weak prostate boundaries, speckle noise and the narrow range of gray levels, leading most image segmentation methods to perform poorly. The enhancement of ultrasound images is challenging, however prostate segmentation can be effectively improved in contrast enhanced images. Anisotropic diffusion has been used for image analysis based on selective smoothness or enhancement of local features such as region boundaries. In its formal form, anisotropic diffusion tends to encourage within-region smoothness and avoid diffusion across different regions. In this paper we extend the anisotropic diffusion to multiple directions such that segmentation methods can effectively be applied based on rich extracted features. A preliminary segmentation method based on extended diffusion is proposed. Finally an adaptive anisotropic diffusion is introduced based on image statistics.
Anisotropic Diffusion for Medical Image EnhancementCSCJournals
Advances in digital imaging techniques have made possible the acquisition of large volumes of Transrectal Ultrasound (TRUS) prostate images so that there is considerable demand for automated segmentation. Prostate cancer diagnosis and treatment rely on segmentation of these Transrectal Ultrasound (TRUS) prostate images, a challenging and difficult task due to weak prostate boundaries, speckle noise and the narrow range of gray levels, leading most image segmentation methods to perform poorly. The enhancement of ultrasound images is challenging, however prostate segmentation can be effectively improved in contrast enhanced images. Anisotropic diffusion has been used for image analysis based on selective smoothness or enhancement of local features such as region boundaries. In its formal form, anisotropic diffusion tends to encourage within-region smoothness and avoid diffusion across different regions. In this paper we extend the anisotropic diffusion to multiple directions such that segmentation methods can effectively be applied based on rich extracted features. A preliminary segmentation method based on extended diffusion is proposed. Finally an adaptive anisotropic diffusion is introduced based on image statistics.
2. Yanira e Pedro debuxan nun lado ós que non reciclan, cun contedor pechado, e noutro lado os que si, cun contedor aberto.
3. Candela e Álvaro debuxan os que si reciclan coa man levantada, os que non reciclan coa man baixada.
4. Anira e Mario debuxan os que si reciclan de cor verde, os que non reciclan de cor vermella. Debuxan só dous nenos/as, indicando enriba o nº que reciclan ou non.
8. Os que si reciclan a un lado e os que non reciclan a outro.
9. Eliximos a proposta que separa a un lado os que si reciclan e a outro lado os que non. Colocamos os gomets uns enriba doutros porque chegan máis arriba. Estamos moi contentos/as do traballo de “maiores” que fixemos!!
10. Conclusións: -O curso que máis recicla é 3º: 19 si/2 non -O curso que menos recicla é 6º:14 si/8 non -As familias do colexio A Rúa reciclan bastante: 88 reciclan (contamos tódolos gomets verdes) 32 non reciclan (contamos tódolos gomets vermellos)
