From Goya to Afghanistan. An essay on the ratio and ethics of medical war pictures

For centuries pictures of the dead and wounded have been part and parcel of war communications. Often the intentions were clear, ranging from medical instructions to anti-war protests. The public's response could coincide with or diverge from the publisher's intention. Following the invention of photography in the nineteenth century, and the subsequent claim of realism, the veracity of medical war images became more complex. Analysing and understanding such photographs have become an ethical obligation with democratic implications. We performed a multidisciplinary analysis of War Surgery (2008), a book containing harsh, full-colour photographs of mutilated soldiers from the Iraq and Afghanistan wars. Our analysis shows that, within the medical context, this book is a major step forward in medical war communication and documentation. In the military context the book can be conceived as an attempt to put matters right given the enormous sacrifice some individuals have suffered. For the public, the relationship between the 'reality' and 'truth' of such photographs is ambiguous, because only looking at the photographs without reading the medical context is limiting. If the observer is not familiar with medical practice, it is difficult for him to fully assess, signify and acknowledge the value and relevance of this book. We therefore assert the importance of the role of professionals and those in the humanities in particular in educating the public and initiating debate. © 2010 Taylor & Francis

Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.

Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature