OPERASI MORFOLOGI UNTUK MENDETEKSI KEBERADAAN BENDA TAJAM PADA CITRA X-RAY DI BANDARA

Pemeriksaan barang bawaan penumpang adalah hal yang mutlak dilakukan sebelum seseorang memasuki kabin pesawat untuk mengantisipasi masuknya benda berbahaya kedalam pesawat. Penentuan adanya benda berbahaya dalam tas bawaan penumpang dilakukan oleh petugas security  dengan mengamati monitor pada mesin x-ray bandara. Faktor kelelahan petugas sangat mempengaruhi tingkat akurasi pada proses pemeriksaan tersebut.  Sehingga pada penelitian ini dibuat perangkat lunak yang dapat diaplikasikan pada mesin x-ray guna membantu petugas security dalam menentukan adanya benda tajam yang diindikasikan sebagai barang berbahaya. Proses deteksi benda tajam diawali dengan segmentasi menggunakan Color base, proses filtering menggunakan Morfologi serta penentuan tajam atau tidaknya objek menggunakan Round Value

An image retrieval approach to setup difficulty levels in training systems for endomicroscopy diagnosis

International audienceLearning medical image interpretation is an evolutive process that requires modular training systems, from non-expert to expert users. Our study aims at developing such a system for endomicroscopy diagnosis. It uses a difficulty predictor to try and shorten the physician learning curve. As the understanding of video diagnosis is driven by visual similarities, we propose a content-based video retrieval approach to estimate the level of interpretation difficulty. The performance of our retrieval method is compared with several state of the art methods, and its genericity is demonstrated with two different clinical databases, on the Barrett's Esophagus and on colonic polyps. From our retrieval results, we learn a difficulty predictor against a ground truth given by the percentage of false diagnoses among several physicians. Our experiments show that, although our datasets are not large enough to test for statistical significance, there is a noticeable relationship between our retrieval-based difficulty estimation and the difficulty experienced by the physicians

Automated X-ray image analysis for cargo security: Critical review and future promise

We review the relatively immature field of automated image analysis for X-ray cargo imagery. There is increasing demand for automated analysis methods that can assist in the inspection and selection of containers, due to the ever-growing volumes of traded cargo and the increasing concerns that customs- and security-related threats are being smuggled across borders by organised crime and terrorist networks. We split the field into the classical pipeline of image preprocessing and image understanding. Preprocessing includes: image manipulation; quality improvement; Threat Image Projection (TIP); and material discrimination and segmentation. Image understanding includes: Automated Threat Detection (ATD); and Automated Contents Verification (ACV). We identify several gaps in the literature that need to be addressed and propose ideas for future research. Where the current literature is sparse we borrow from the single-view, multi-view, and CT X-ray baggage domains, which have some characteristics in common with X-ray cargo

A statistical approach for image difficulty estimation in x-ray screening using image measurements

The relevance of aviation security has increased dramatically at the beginning of this century. One of the most important tasks is the visual inspection of passenger bags using x-ray machines. In this study, we investigated the role of image based factors on human detection of prohibited items in x-ray images. Schwaninger, Hardmeier, and Hofer (2004, 2005) have identified three image based factors: View Difficulty, Superposition and Bag Complexity. This article consists of 4 experiments which lead to the development of a statistical model that is able to predict image difficulty based on these image based factors. Experiment 1 is a replication of earlier findings confirming the relevance of image based factors as defined by Schwaninger et al. (2005) on x-ray detection performance. In Experiment 2, we found significant correlations between human ratings of image based factors and human detection performance. In Experiment 3, we introduced our image measurements and found significant correlations between them a nd human detection performance. Moreover, significant correlations were found between our image measurements and corresponding human ratings, indicating high perceptual plausibility. In Experiment 4, it was shown using multiple linear regression analysis that our image measurements can predict human performance as well as human ratings can. Applications of a computational model for threat image projection systems and for adaptive computer-based training are discussed