Multifocal small bowel stromal tumours presenting with peritonitis in an HIV positive patient

AbstractINTRODUCTIONThe most common mesenchymal tumour of the gastrointestinal tract is stromal tumours (GISTs). Symptomatic GISTs can present with complications such as haemorrhage, obstruction and perforation. Complete surgical resection with negative margins is the mainstay of treatment but may be imprudent on emergent occasion. Tyrosine-kinase inhibitors (TKIs) have been revolutionary in the treatment of GISTs and have resulted in improved outcomes.PRESENTATION OF CASEA 41 year old HIV positive male presented with an acute history of abdominal pain and obstructive symptoms. Clinical examination revealed sepsis and peritonitis. One of the several small bowel tumours discovered at exploratory laparotomy was necrotic and perforated. The perforated tumour alone was resected and a small bowel internal hernia reduced. The patient made an uneventful recovery and will be considered for TKI therapy with a view to later re-operation.DISCUSSIONGISTs very rarely perforate. The pathophysiology of stromal tumour necrosis is poorly understood. Multifocality and small bowel location are poor prognosticators and may occur in the setting of familial GISTs, specific syndromes and sporadic cases. There is no established association between HIV and GISTs.CONCLUSIONPerforation occurs infrequently in ≤8% of symptomatic cases and poses increased risk of local recurrence. The surgical management of perforation takes precedence in an emergency. The surgeon must however take cognisance of the adherence to ideal oncologic principles where feasible. TKI therapy is invaluable if a re-exploration is to be later considered

A Comprehensive Review of Distributed Coding Algorithms for Visual Sensor Network (VSN)

Since the invention of low cost camera, it has been widely incorporated into the sensor node in Wireless Sensor Network (WSN) to form the Visual Sensor Network (VSN). However, the use of camera is bringing with it a set of new challenges, because all the sensor nodes are powered by batteries. Hence, energy consumption is one of the most critical issues that have to be taken into consideration. In addition to this, the use of batteries has also limited the resources (memory, processor) that can be incorporated into the sensor node. The life time of a VSN decreases quickly as the image is transferred to the destination. One of the solutions to the aforementioned problem is to reduce the data to be transferred in the network by using image compression. In this paper, a comprehensive survey and analysis of distributed coding algorithms that can be used to encode images in VSN is provided. This also includes an overview of these algorithms, together with their advantages and deficiencies when implemented in VSN. These algorithms are then compared at the end to determine the algorithm that is more suitable for VSN

Permutation entropy based full-reference image quality qssessment

Due to the increasing proliferation of multimedia signals, specifically, image, video and their applications in our daily life, it is indispensable to have methods that can efficiently predict the visual quality of images with high measures of accuracy. Image processing procedures often introduce undesirable distortion in images that require fixing; preferably consistent with a human visual system (HVS). Therefore, an image quality assessment(IQA) framework should be highly accurate as well as computationally efficient; making it viable to be used with different image processing applications, especially, with real-time applications. Motivated by the need of appropriate objective models, we propose a novel objective IQA algorithm, namely, Permutation Entropy Deviation Index (PEDI), based on the working principle of permutation entropy (PE). Permutation entropy helps in detecting and visualizing changes related to structures with the correlation between successive samples instead of considering magnitudes of the signal, and since, perception of an image to the HVS changes more because of structural changes in an image rather than that of visible errors i.e. MSE. Therefore, in this work, we have exploited this property to predict image quality efficiently. Moreover, entropy itself is sensitive to variations, whereas the permutation entropy captures pattern variations in an image. Furthermore, each local patch in the distorted image undergoes a different level of distortion due to structural differences. This motivates us to use permutation entropy to exploit the global variations in the local quality map for image quality assessment. With standard deviation as the pooling strategy, we observed that permutation entropy between reference and distorted images could predict image quality with high measures of accuracy. Experimental results on a subjective database, CSIQ, have shown that the proposed model outperforms most of the existing STOA image quality assessment models and highly correlates with subjective judgements