RESUMO
Gertrude Stein was not only a fairly open lesbian but also Jewish, expatriate, and androgynous-all attributes that often retarded mass-market success. Why then was she so popular? The article offers original research highlighting how Stein was constructed as a kind of "opium queen" in the popular American press, and the ways that this decadent, bohemian celebrity persona allowed her to operate as "broadly queer" rather than "specifically gay" in the American cultural imaginary-a negotiation that accounts for the mass-market success rather than censure of The Autobiography of Alice B. Toklas despite the unparalleled visibility of its lesbian erotics.
Assuntos
Homossexualidade Feminina/história , Judeus/história , Estilo de Vida/história , Literatura Moderna/história , Meios de Comunicação de Massa/história , Medicina na Literatura , Ópio/história , Religião e Psicologia , Feminino , História do Século XIX , História do Século XX , Humanos , Estados UnidosRESUMO
Delineation and noise removal play a significant role in clinical quantification of PET images. Conventionally, these two tasks are considered independent, however, denoising can improve the performance of boundary delineation by enhancing SNR while preserving the structural continuity of local regions. On the other hand, we postulate that segmentation can help denoising process by constraining the smoothing criteria locally. Herein, we present a novel iterative approach for simultaneous PET image denoising and segmentation. The proposed algorithm uses generalized Anscombe transformation priori to non-local means based noise removal scheme and affinity propagation based delineation. For nonlocal means denoising, we propose a new regional means approach where we automatically and efficiently extract the appropriate subset of the image voxels by incorporating the class information from affinity propagation based segmentation. PET images after denoising are further utilized for refinement of the segmentation in an iterative manner. Qualitative and quantitative results demonstrate that the proposed framework successfully removes the noise from PET images while preserving the structures, and improves the segmentation accuracy.