Steganalytic Methods for the Detection of Histogram Shifting Data Hiding Schemes

Peer-reviewed

Embedding, Extraction and Detection of Digital Watermark in Spectral Images

Tässä diplomityössä tutkitaan tekniikoita, joillavesileima lisätään spektrikuvaan, ja menetelmiä, joilla vesileimat tunnistetaanja havaitaan spektrikuvista. PCA (Principal Component Analysis) -algoritmia käyttäen alkuperäisten kuvien spektriulottuvuutta vähennettiin. Vesileiman lisääminen spektrikuvaan suoritettiin muunnosavaruudessa. Ehdotetun mallin mukaisesti muunnosavaruuden komponentti korvattiin vesileiman ja toisen muunnosavaruuden komponentin lineaarikombinaatiolla. Lisäyksessä käytettävää parametrijoukkoa tutkittiin. Vesileimattujen kuvien laatu mitattiin ja analysoitiin. Suositukset vesileiman lisäykseen esitettiin. Useita menetelmiä käytettiin vesileimojen tunnistamiseen ja tunnistamisen tulokset analysoitiin. Vesileimojen kyky sietää erilaisia hyökkäyksiä tarkistettiin. Diplomityössä suoritettiin joukko havaitsemis-kokeita ottamalla huomioon vesileiman lisäyksessä käytetyt parametrit. ICA (Independent Component Analysis) -menetelmää pidetään yhtenä mahdollisena vaihtoehtona vesileiman havaitsemisessa.

Supervised localimage feature detection

This thesis is about detection of local image features. The research topic belongs to the wider area of object detection, which is a machine vision and pattern recognition problem where an object must be detected (located) in an image. State-of-the-art object detection methods often divide the problem into separate interest point detection and local image description steps, but in this thesis a different technique is used, leading to higher quality image features which enable more precise localization. Instead of using interest point detection the landmark positions are marked manually. Therefore, the quality of the image features is not limited by the interest point detection phase and the learning of image features is simplified. The approach combines both interest point detection and local description into one phase for detection. Computational efficiency of the descriptor is therefore important, leaving out many of the commonly used descriptors as unsuitably heavy. Multiresolution Gabor features has been the main descriptor in this thesis and improving their efficiency is a significant part. Actual image features are formed from descriptors by using a classifierwhich can then recognize similar looking patches in new images. The main classifier is based on Gaussian mixture models. Classifiers are used in one-class classifier configuration where there are only positive training samples without explicit background class. The local image feature detection method has been tested with two freely available face detection databases and a proprietary license plate database. The localization performance was very good in these experiments. Other applications applying the same under-lying techniques are also presented, including object categorization and fault detection.

Environmental Monitoring using Image Analysis

Tärkeä tehtävä ympäristön tarkkailussa on arvioida ympäristön nykyinen tila ja ihmisen siihen aiheuttamat muutokset sekä analysoida ja etsiä näiden yhtenäiset suhteet. Ympäristön muuttumista voidaan hallita keräämällä ja analysoimalla tietoa. Tässä diplomityössä on tutkittu vesikasvillisuudessa hai vainuja muutoksia käyttäen etäältä hankittua mittausdataa ja kuvan analysointimenetelmiä. Ympäristön tarkkailuun on käytetty Suomen suurimmasta järvestä Saimaasta vuosina 1996 ja 1999 otettuja ilmakuvia. Ensimmäinen kuva-analyysin vaihe on geometrinen korjaus, jonka tarkoituksena on kohdistaa ja suhteuttaa otetut kuvat samaan koordinaattijärjestelmään. Toinen vaihe on kohdistaa vastaavat paikalliset alueet ja tunnistaa kasvillisuuden muuttuminen. Kasvillisuuden tunnistamiseen on käytetty erilaisia lähestymistapoja sisältäen valvottuja ja valvomattomia tunnistustapoja. Tutkimuksessa käytettiin aitoa, kohinoista mittausdataa, minkä perusteella tehdyt kokeet antoivat hyviä tuloksia tutkimuksen onnistumisesta.

Label-free detection of tobramycin in serum by transmission-localized surface plasmon resonance.

In order to improve the efficacy and safety of treatments, drug dosage needs to be adjusted to the actual needs of each patient in a truly personalized medicine approach. Key for widespread dosage adjustment is the availability of point-of-care devices able to measure plasma drug concentration in a simple, automated, and cost-effective fashion. In the present work, we introduce and test a portable, palm-sized transmission-localized surface plasmon resonance (T-LSPR) setup, comprised of off-the-shelf components and coupled with DNA-based aptamers specific to the antibiotic tobramycin (467 Da). The core of the T-LSPR setup are aptamer-functionalized gold nanoislands (NIs) deposited on a glass slide covered with fluorine-doped tin oxide (FTO), which acts as a biosensor. The gold NIs exhibit localized plasmon resonance in the visible range matching the sensitivity of the complementary metal oxide semiconductor (CMOS) image sensor employed as a light detector. The combination of gold NIs on the FTO substrate, causing NIs size and pattern irregularity, might reduce the overall sensitivity but confers extremely high stability in high-ionic solutions, allowing it to withstand numerous regeneration cycles without sensing losses. With this rather simple T-LSPR setup, we show real-time label-free detection of tobramycin in buffer, measuring concentrations down to 0.5 μM. We determined an affinity constant of the aptamer-tobramycin pair consistent with the value obtained using a commercial propagating-wave based SPR. Moreover, our label-free system can detect tobramycin in filtered undiluted blood serum, measuring concentrations down to 10 μM with a theoretical detection limit of 3.4 μM. While the association signal of tobramycin onto the aptamer is masked by the serum injection, the quantification of the captured tobramycin is possible during the dissociation phase and leads to a linear calibration curve for the concentrations over the tested range (10-80 μM). The plasmon shift following surface binding is calculated in terms of both plasmon peak location and hue, with the latter allowing faster data elaboration and real-time display of the results. The presented T-LSPR system shows for the first time label-free direct detection and quantification of a small molecule in the complex matrix of filtered undiluted blood serum. Its uncomplicated construction and compact size, together with the remarkable performances, represent a leap forward toward effective point-of-care devices for therapeutic drug concentration monitoring.

Detection of Clinically Significant Prostate Cancer Using Magnetic Resonance Imaging-Ultrasound Fusion Targeted Biopsy: A Systematic Review.

CONTEXT: The current standard for diagnosing prostate cancer in men at risk relies on a transrectal ultrasound-guided biopsy test that is blind to the location of the cancer. To increase the accuracy of this diagnostic pathway, a software-based magnetic resonance imaging-ultrasound (MRI-US) fusion targeted biopsy approach has been proposed. OBJECTIVE: Our main objective was to compare the detection rate of clinically significant prostate cancer with software-based MRI-US fusion targeted biopsy against standard biopsy. The two strategies were also compared in terms of detection of all cancers, sampling utility and efficiency, and rate of serious adverse events. The outcomes of different targeted approaches were also compared. EVIDENCE ACQUISITION: We performed a systematic review of PubMed/Medline, Embase (via Ovid), and Cochrane Review databases in December 2013 following the Preferred Reported Items for Systematic reviews and Meta-analysis statement. The risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. EVIDENCE SYNTHESIS: Fourteen papers reporting the outcomes of 15 studies (n=2293; range: 13-582) were included. We found that MRI-US fusion targeted biopsies detect more clinically significant cancers (median: 33.3% vs 23.6%; range: 13.2-50% vs 4.8-52%) using fewer cores (median: 9.2 vs 37.1) compared with standard biopsy techniques, respectively. Some studies showed a lower detection rate of all cancer (median: 50.5% vs 43.4%; range: 23.7-82.1% vs 14.3-59%). MRI-US fusion targeted biopsy was able to detect some clinically significant cancers that would have been missed by using only standard biopsy (median: 9.1%; range: 5-16.2%). It was not possible to determine which of the two biopsy approaches led most to serious adverse events because standard and targeted biopsies were performed in the same session. Software-based MRI-US fusion targeted biopsy detected more clinically significant disease than visual targeted biopsy in the only study reporting on this outcome (20.3% vs 15.1%). CONCLUSIONS: Software-based MRI-US fusion targeted biopsy seems to detect more clinically significant cancers deploying fewer cores than standard biopsy. Because there was significant study heterogeneity in patient inclusion, definition of significant cancer, and the protocol used to conduct the standard biopsy, these findings need to be confirmed by further large multicentre validating studies. PATIENT SUMMARY: We compared the ability of standard biopsy to diagnose prostate cancer against a novel approach using software to overlay the images from magnetic resonance imaging and ultrasound to guide biopsies towards the suspicious areas of the prostate. We found consistent findings showing the superiority of this novel targeted approach, although further high-quality evidence is needed to change current practice.

Eye-tracking of nodule detection in lung CT volumetric data.

PURPOSE: Signal detection on 3D medical images depends on many factors, such as foveal and peripheral vision, the type of signal, and background complexity, and the speed at which the frames are displayed. In this paper, the authors focus on the speed with which radiologists and naïve observers search through medical images. Prior to the study, the authors asked the radiologists to estimate the speed at which they scrolled through CT sets. They gave a subjective estimate of 5 frames per second (fps). The aim of this paper is to measure and analyze the speed with which humans scroll through image stacks, showing a method to visually display the behavior of observers as the search is made as well as measuring the accuracy of the decisions. This information will be useful in the development of model observers, mathematical algorithms that can be used to evaluate diagnostic imaging systems. METHODS: The authors performed a series of 3D 4-alternative forced-choice lung nodule detection tasks on volumetric stacks of chest CT images iteratively reconstructed in lung algorithm. The strategy used by three radiologists and three naïve observers was assessed using an eye-tracker in order to establish where their gaze was fixed during the experiment and to verify that when a decision was made, a correct answer was not due only to chance. In a first set of experiments, the observers were restricted to read the images at three fixed speeds of image scrolling and were allowed to see each alternative once. In the second set of experiments, the subjects were allowed to scroll through the image stacks at will with no time or gaze limits. In both static-speed and free-scrolling conditions, the four image stacks were displayed simultaneously. All trials were shown at two different image contrasts. RESULTS: The authors were able to determine a histogram of scrolling speeds in frames per second. The scrolling speed of the naïve observers and the radiologists at the moment the signal was detected was measured at 25-30 fps. For the task chosen, the performance of the observers was not affected by the contrast or experience of the observer. However, the naïve observers exhibited a different pattern of scrolling than the radiologists, which included a tendency toward higher number of direction changes and number of slices viewed. CONCLUSIONS: The authors have determined a distribution of speeds for volumetric detection tasks. The speed at detection was higher than that subjectively estimated by the radiologists before the experiment. The speed information that was measured will be useful in the development of 3D model observers, especially anthropomorphic model observers which try to mimic human behavior.

Prospective motion correction with FID-triggered image navigators

In this work, we propose a method for prospective motion correction in MRI using a novel image navigator module, which is triggered by a free induction decay (FID) navigator. Only when motion occurs, the image navigator is run and new positional information is obtained through image registration. The image navigator was specifically designed to match the impact on the magnetization and the acoustic noise of the host sequence. This detection-correction scheme was implemented for an MP-RAGE sequence and 5 healthy volunteers were scanned at 3T while performing various head movements. The correction performance was demonstrated through automated brain segmentation and an image quality index whose results are sensitive to motion artifacts.

Anthropomorphic model observer performance in three-dimensional detection task for low-contrast computed tomography.

X-ray medical imaging is increasingly becoming three-dimensional (3-D). The dose to the population and its management are of special concern in computed tomography (CT). Task-based methods with model observers to assess the dose-image quality trade-off are promising tools, but they still need to be validated for real volumetric images. The purpose of the present work is to evaluate anthropomorphic model observers in 3-D detection tasks for low-contrast CT images. We scanned a low-contrast phantom containing four types of signals at three dose levels and used two reconstruction algorithms. We implemented a multislice model observer based on the channelized Hotelling observer (msCHO) with anthropomorphic channels and investigated different internal noise methods. We found a good correlation for all tested model observers. These results suggest that the msCHO can be used as a relevant task-based method to evaluate low-contrast detection for CT and optimize scan protocols to lower dose in an efficient way.

Hardcopy quality parameters to ensure structures detection at digital mammography

Objective To develop procedures to ensure consistency of printing quality of digital images, by means of hardcopy quantitative analysis based on a standard image. Materials and Methods Characteristics of mammography DI-ML and general purpose DI-HL films were studied through the QC-Test utilizing different processing techniques in a FujiFilm®-DryPix4000 printer. A software was developed for sensitometric evaluation, generating a digital image including a gray scale and a bar pattern to evaluate contrast and spatial resolution. Results Mammography films showed maximum optical density of 4.11 and general purpose films, 3.22. The digital image was developed with a 33-step wedge scale and a high-contrast bar pattern (1 to 30 lp/cm) for spatial resolution evaluation. Conclusion Mammographic films presented higher values for maximum optical density and contrast resolution as compared with general purpose films. The utilized digital processing technique could only change the image pixels matrix values and did not affect the printing standard. The proposed digital image standard allows greater control of the relationship between pixels values and optical density obtained in the analysis of films quality and printing systems.

Image processing of false identity documents for forensic intelligence

Forensic intelligence has recently gathered increasing attention as a potential expansion of forensic science that may contribute in a wider policing and security context. Whilst the new avenue is certainly promising, relatively few attempts to incorporate models, methods and techniques into practical projects are reported. This work reports a practical application of a generalised and transversal framework for developing forensic intelligence processes referred to here as the Transversal model adapted from previous work. Visual features present in the images of four datasets of false identity documents were systematically profiled and compared using image processing for the detection of a series of modus operandi (M.O.) actions. The nature of these series and their relation to the notion of common source was evaluated with respect to alternative known information and inferences drawn regarding respective crime systems. 439 documents seized by police and border guard authorities across 10 jurisdictions in Switzerland with known and unknown source level links formed the datasets for this study. Training sets were developed based on both known source level data, and visually supported relationships. Performance was evaluated through the use of intra-variability and inter-variability scores drawn from over 48,000 comparisons. The optimised method exhibited significant sensitivity combined with strong specificity and demonstrates its ability to support forensic intelligence efforts.

Examining the Role of Rebranding in Perceived Image of a Product Line

The main objective for this study was to explore certain organization’s product line rebranding process and its impact on product line’s perceived image. The case company is a global paper, packaging and forest products company, business segment paper board. The audience explored is one of the company’s major customers, merchant in Germany. The research was performed as a descriptive case study with a purpose to provide longitudinal insight into the product line image and its eventual alteration as a result of the case company’s rebranding process. Mainly qualitative methods were used for conducting the research. The data for the empirical part was collected with a web-based survey at two different points of time; before the rebranded products entered the market and after they had been available approximately six months. The results of this study reveal that the case company has performed well in its attempt to improve product line’s brand image through rebranding. It was found that between the two brand image measurements the product brand image seems to have improved in all of the areas which according to theoretical framework of this study contribute to formation of brand image; brand associations, marketing communications and interpersonal relationships, not forgetting the original platform that initiated the change; technical quality modifications. In other words it may be concluded that as technical quality was brought to a new level, also assessments about the brand image improved respectively.

A Technique for Digital Color Image Watermarking Using ICA

With the increase of use of digital media the need for the methods of multimedia protection becomes extremely important. The number of the solutions to the problem from encryption to watermarking is large and is growing every year. In this work digital image watermarking is considered, specifically a novel method of digital watermarking of color and spectral images. An overview of existing methods watermarking of color and grayscale images is given in the paper. Methods using independent component analysis (ICA) for detection and the ones using discrete wavelet transform (DWT) and discrete cosine transform (DCT) are considered in more detail. A novel method of watermarking proposed in this paper allows embedding of a color or spectral watermark image into color or spectral image consequently and successful extraction of the watermark out of the resultant watermarked image. A number of experiments have been performed on the quality of extraction depending on the parameters of the embedding procedure. Another set of experiments included the test of the robustness of the algorithm proposed. Three techniques have been chosen for that purpose: median filter, low-pass filter (LPF) and discrete cosine transform (DCT), which are a part of a widely known StirMark - Image Watermarking Robustness Test. The study shows that the proposed watermarking technique is fragile, i.e. watermark is altered by simple image processing operations. Moreover, we have found that the contents of the image to be watermarked do not affect the quality of the extraction. Mixing coefficients, that determine the amount of the key and watermark image in the result, should not exceed 1% of the original. The algorithm proposed has proven to be successful in the task of watermark embedding and extraction.

Detection of soybean planted areas through orbital images based on culture spectral dynamics

The soybean is important to the economy of Brazil, so the estimation of the planted area and the production with higher antecedence and reliability becomes essential. Techniques related to Remote Sensing may help to obtain this information at lower cost and less subjectivity in relation to traditional surveys. The aim of this study is to estimate the planted area with soybean culture in the crop of 2008/2009 in cities in the west of the state of Paraná, in Brazil, based on the spectral dynamics of the culture and through the use of the specific system of analysis for images of Landsat 5/TM satellite. The obtained results were satisfactory, because the classification supervised by Maximum Verisimilitude - MaxVer along with the techniques of the specific system of analysis for satellite images has allowed an estimate of soybean planted area (soybean mask), obtaining values ​​of the metrics of Global Accuracy with an average of 79.05% and Kappa Index over 63.50% in all cities. The monitoring of a reference area was of great importance for determining the vegetative phase in which the culture is more different from the other targets, facilitating the choice of training samples (ROIs) and avoiding misclassifications.

Automatic image-based identification of Saimaa ringed seals

The Saimaa ringed seal is one of the most endangered seals in the world. It is a symbol of Lake Saimaa and a lot of effort have been applied to save it. Traditional methods of seal monitoring include capturing the animals and installing sensors on their bodies. These invasive methods for identifying can be painful and affect the behavior of the animals. Automatic identification of seals using computer vision provides a more humane method for the monitoring. This Master's thesis focuses on automatic image-based identification of the Saimaa ringed seals. This consists of detection and segmentation of a seal in an image, analysis of its ring patterns, and identification of the detected seal based on the features of the ring patterns. The proposed algorithm is evaluated with a dataset of 131 individual seals. Based on the experiments with 363 images, 81\% of the images were successfully segmented automatically. Furthermore, a new approach for interactive identification of Saimaa ringed seals is proposed. The results of this research are a starting point for future research in the topic of seal photo-identification.