A Novel Scheme for Intelligent Recognition of Pornographic Images

Harmful contents are rising in internet day by day and this motivates the essence of more research in fast and reliable obscene and immoral material filtering. Pornographic image recognition is an important component in each filtering system. In this paper, a new approach for detecting pornographic images is introduced. In this approach, two new features are suggested. These two features in combination with other simple traditional features provide decent difference between porn and non-porn images. In addition, we applied fuzzy integral based information fusion to combine MLP (Multi-Layer Perceptron) and NF (Neuro-Fuzzy) outputs. To test the proposed method, performance of system was evaluated over 18354 download images from internet. The attained precision was 93% in TP and 8% in FP on training dataset, and 87% and 5.5% on test dataset. Achieved results verify the performance of proposed system versus other related works

Fast and Effective Bag-of-Visual-Word Model to Pornographic Images Recognition Using the FREAK Descriptor

Recently, the Bag of Visual Word (BoVW) has gained enormous popularity between researchers to object recognition. Pornographic image recognition with respect to computational complexity, appropriate accuracy, and memory consumption is a major challenge in the applications with time constraints such as the internet pornography filtering. Most of the existing researches based on the Bow, using the very popular SIFT and SURF algorithms to description and match detected keypoints in the image. The main problem of these methods is high computational complexity due to constructing the high dimensional feature vectors. This research proposed a BoVW based model by adopting very fast and simple binary descriptor FREAK to speed-up pornographic recognition process. Meanwhile, the keypoints are detected in the ROI of images which improves the recognition speed due to eliminating many noise keypoints placed in the image background. Finally, in order to find the most representational visual-vocabulary, different vocabularies are generated from size 150 to 500 for BoVW. Compared with the similar works, the experimental results show that the proposed model has gained remarkable improvement in the terms of computational complexity

Novel Techniques for Automated Dental Identification

Automated dental identification is one of the best candidates for postmortem identification. With the large number of victims encountered in mass disasters, automating the process of postmortem identification is receiving an increased attention. This dissertation introduces new approaches for different stages of Automated Dental Identification system: These stages include segmentations, classification, labeling, and matching:;We modified the seam carving technique to adapt the problem of segmenting dental image records into individual teeth. We propose a two-stage teeth segmentation approach for segmenting the dental images. In the first stage, the teeth images are preprocessed by a two-step thresholding technique, which starts with an iterative thresholding followed by an adaptive thresholding to binarize the teeth images. In the second stage, we adapt the seam carving technique on the binary images, using both horizontal and vertical seams, to separate each individual tooth. We have obtained an optimality rate of 54.02% for the bitewing type images, which is superior to all existing fully automated dental segmentation algorithms in the literature, and a failure rate of 1.05%. For the periapical type images, we have obtained a high optimality rate of 58.13% and a low failure rate of 0.74 which also surpasses the performance of existing techniques. An important problem in automated dental identification is automatic classification of teeth into four classes (molars, premolars, canines, and incisors). A dental chart is a key to avoiding illogical comparisons that inefficiently consume the limited computational resources, and may mislead decision-making. We tackle this composite problem using a two-stage approach. The first stage, utilizes low computational-cost, appearance-based features, using Orthogonal Locality Preserving Projections (OLPP) for assigning an initial class. The second stage applies a string matching technique, based on teeth neighborhood rules, to validate initial teeth-classes and hence to assign each tooth a number corresponding to its location in the dental chart, even in the presence of a missed tooth. The experimental results of teeth classification show that on a large dataset of bitewing and periapical films, the proposed approach achieves overall classification accuracy of 77% and teeth class validation enhances the overall teeth classification accuracy to 87% which is slightly better than the performance obtained from previous methods based on EigenTeeth the performance of which is 75% and 86%, respectively.;We present a new technique that searches the dental database to find a candidate list. We use dental records of the FBI\u27s Criminal Justice Service (CJIC) ADIS database, that contains 104 records (about 500 bitewing and periapical films) involving more than 2000 teeth, 47 Antemortem (AM) records and 57 Postmortem (PM) records with 20 matched records.;The proposed approach consists of two main stages, the first stage is to preprocess the dental records (segmentation and teeth labeling classification) in order to get a reliable, appearance-based, low computational-cost feature. In the second stage, we developed a technique based on LaplacianTeeth using OLPP algorithm to produce a candidate list. The proposed technique can correctly retrieve the dental records 65% in the 5 top ranks while the method based on EigenTeeth remains at 60%. The proposed approach takes about 0.17 seconds to make record to record comparison while the other method based on EigenTeeth takes about 0.09 seconds.;Finally, we address the teeth matching problem by presenting a new technique for dental record retrieval. The technique is based on the matching of the Scale Invariant feature Transform (SIFT) descriptors guided by the teeth contour between the subject and reference dental records. Our fundamental objective is to accomplish a relatively short match list, with a high probability of having the correct match reference. The proposed technique correctly retrieves the dental records with performance rates of 35% and 75% in the 1 and 5 top ranks respectively, and takes only an average time of 4.18 minutes to retrieve a match list. This compares favorably with the existing technique shape-based (edge direction histogram) method which has the performance rates of 29% and 46% in the 1 and 5 top ranks respectively.;In summary, the proposed ADIS system accurately retrieves the dental record with an overall rate of 80% in top 5 ranks when a candidate list of 20 is used (from potential match search) whereas a candidate size of 10 yields an overall rate of 84% in top 5 ranks and takes only a few minutes to search the database, which compares favorably against most of the existing methods in the literature, when both accuracy and computational complexity are considered

PadChest: A large chest x-ray image dataset with multi-label annotated reports

We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports. This dataset includes more than 160,000 images obtained from 67,000 patients that were interpreted and reported by radiologists at Hospital San Juan Hospital (Spain) from 2009 to 2017, covering six different position views and additional information on image acquisition and patient demography. The reports were labeled with 174 different radiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and mapped onto standard Unified Medical Language System (UMLS) terminology. Of these reports, 27% were manually annotated by trained physicians and the remaining set was labeled using a supervised method based on a recurrent neural network with attention mechanisms. The labels generated were then validated in an independent test set achieving a 0.93 Micro-F1 score. To the best of our knowledge, this is one of the largest public chest x-ray database suitable for training supervised models concerning radiographs, and the first to contain radiographic reports in Spanish. The PadChest dataset can be downloaded from http://bimcv.cipf.es/bimcv-projects/padchest/

Speech perception in MRI scanner noise by persons with aphasia

Purpose. To examine reductions in performance on auditory tasks by aphasic and neurologically-intact individuals as a result of concomitant MRI scanner noise. Methods. Four tasks together forming a continuum of linguistic complexity were developed. They included complex-tone pitch discrimination, same/different discrimination of minimal pair syllables, lexical decision, and sentence plausibility. Each task was performed by persons with aphasia (PWA) and by controls. The stimuli were presented in silence and also in the noise recorded from within the bore of a 3T MRI scanner at three signal-to-noise ratios (S/Ns). Results. Across the four tasks, the PWA scored lower than the controls and performance fell as a function of decreased S/N. However, the rate at which performance fell was not different across the two listener groups in any task. Conclusions. Depending upon the relative levels of the signals and noise, the intense noise accompanying MRI scanning has the potential to severely disrupt performance. However, PWA are no more susceptible to the disruptive influence of this noise than are unimpaired individuals usually employed as controls. Thus, fMRI data from aphasic and control individuals may be interpreted without complications associated with large interactions between scanner noise and performance reduction

A non-invasive approach to monitor chronic lymphocytic leukemia engraftment in a xenograft mouse model using ultra-small superparamagnetic iron oxide-magnetic resonance imaging (USPIO-MRI).

This work was supported by: Associazione Italiana Ricerca sul Cancro (AIRC) [Grant 5 x mille n.9980, (to M.F., F.M. and A. N.)]; AIRC I.G. [n. 14,326 (to M.F.)], [n.10136 and 16,722 (A.N.)], [n.15426 (to F.F.)]. AIRC and Fondazione CaRiCal co-financed Multi Unit Regional Grant 2014 [n.16695 (to F.M.)]. Italian Ministry of Health 5 × 1000 funds (to F.F). A.G R. was supported by Associazione Italiana contro le Leucemie-Linfomi-Mielomi (AIL) Cosenza - Fondazione Amelia Scorza (FAS). S.M. C.M., F.V., L. E., S. B., were supported by AIRC.Peer reviewedPostprin