The proliferation of Infrared technology and imaging systems enables a different perspective to tackle many computer
vision problems in defense and security applications. Infrared images are widely used by the law enforcement, Homeland
Security and military organizations to achieve a significant advantage or situational awareness, and thus is vital to protect
these data against malicious attacks. Concurrently, sophisticated malware are developed which are able to disrupt the
security and integrity of these digital media. For instance, illegal distribution and manipulation are possible malicious
attacks to the digital objects. In this paper we explore the use of a new layer of defense for the integrity of the infrared
images through the aid of information hiding techniques such as watermarking. In this context, we analyze the efficiency
of several optimal decoding schemes for the watermark inserted into the Singular Value Decomposition (SVD) domain of
the IR images using an additive spread spectrum (SS) embedding framework. In order to use the singular values (SVs) of
the IR images with the SS embedding we adopt several restrictions that ensure that the values of the SVs will maintain
their statistics. For both the optimal maximum likelihood decoder and sub-optimal decoders we assume that the PDF of
SVs can be modeled by the Weibull distribution. Furthermore, we investigate the challenges involved in protecting and
assuring the integrity of IR images such as data complexity and the error probability behavior, i.e., the probability of
detection and the probability of false detection, for the applied optimal decoders. By taking into account the efficiency and
the necessary auxiliary information for decoding the watermark, we discuss the suitable decoder for various operating
situations. Experimental results are carried out on a large dataset of IR images to show the imperceptibility and efficiency
of the proposed scheme against various attack scenarios.
Full-field digital mammography (FFDM) is the most common screening procedure for detecting early breast cancer. However, due to complications such as overlapping breast tissue in projection images, the efficacy of FFDM reading is reduced. Recent studies have shown that digital breast tomosynthesis (DBT), in combination with FFDM, increases detection sensitivity considerably while decreasing false-positive, recall rates. There is a huge interest in creating diagnostically accurate 2-D interpretations from the DBT slices. Most of the 2-D syntheses rely on visualizing the maximum intensities (brightness) from each slice through different methods. We propose a wavelet based fusion method, where we focus on preserving holistic information from larger structures such as masses while adding high frequency information that is relevant and helpful for diagnosis. This method enables the spatial generation of a 2D image from a series of DBT images, each of which contains both smooth and coarse structures distributed in the wavelet domain. We believe that the wavelet-synthesized images, generated from their DBT image datasets, provide radiologists with improved lesion and micro-calcification conspicuity as compared with FFDM images. The potential impact of this fusion method is (1) Conception of a device-independent, data-driven modality that increases the conspicuity of lesions, thereby facilitating early detection and potentially reducing recall rates; (2) Reduction of the accompanying radiation dose to the patient.
In this paper we present a simple technique that can be employed to filter the output of the computerized mass detection schemes. The sensitivity of computer-aided detection (CAD) systems is high; nevertheless specificity is not due to high false positive (FP) detection rates. Our approach is based on Histogram of Oriented Gradients (HOG) descriptor for filtering the mass and normal tissue regions. After the descriptors are computed, Support Vector Machines (SVM) are applied to classify the identified masses. The devised technique was tested on 1881 regions of interest (ROIs) acquired using a previously proposed CAD system. Extensive simulations are conducted to illustrate the capacity of the HOG descriptor to improve the performances of mass detection systems.