Towards a Computed-Aided Diagnosis System in Colonoscopy: Automatic Polyp Segmentation Using Convolution Neural Networks

Early diagnosis is essential for the successful treatment of bowel cancers including colorectal cancer (CRC), and capsule endoscopic imaging with robotic actuation can be a valuable diagnostic tool when combined with automated image analysis. We present a deep learning rooted detection and segmentation framework for recognizing lesions in colonoscopy and capsule endoscopy images. We restructure established convolution architectures, such as VGG and ResNets, by converting them into fully-connected convolution networks (FCNs), fine-tune them and study their capabilities for polyp segmentation and detection. We additionally use shape-from-shading (SfS) to recover depth and provide a richer representation of the tissue’s structure in colonoscopy images. Depth is incorporated into our network models as an additional input channel to the RGB information and we demonstrate that the resulting network yields improved performance. Our networks are tested on publicly available datasets and the most accurate segmentation model achieved a mean segmentation interception over union (IU) of 47.78% and 56.95% on the ETIS-Larib and CVC-Colon datasets, respectively. For polyp detection, the top performing models we propose surpass the current state-of-the-art with detection recalls superior to 90% for all datasets tested. To our knowledge, we present the first work to use FCNs for polyp segmentation in addition to proposing a novel combination of SfS and RGB that boosts performance

VIDEO RECAPTURE DETECTION BASED ON GHOSTING ARTIFACT ANALYSIS

Video forensics is becoming a popular field of research and an increasing number of forensic techniques have been proposed in the last few years. However, a simple yet effective method to fool many detectors consists in recapturing a video sequence with a camcorder. For this reason being able to detect video recapture is a topic of interest for a forensic analyst. In this paper, we first characterize the video recapture model, focusing on the common scenario of a sequence recaptured from a LCD monitor using a digital camcorder, then we propose a recapture detector for this case. The detector is based on the analysis of a characteristic ghosting artifact left by the recapture process. The presented algorithm is finally validated by means of tests on original and recaptured sequences. These tests prove that the algorithm achieves high accuracy results. Index Terms — video forensics, recapturing, ghosting 1