Multimodal fusion of tomographic sequences of medical images: MRE spatially enhanced by MRI.

BACKGROUND AND OBJECTIVE: In biomedical fields, image analysis is often necessary for an accurate diagnosis. In order to obtain all the information needed to form an in-depth clinical picture, it may be useful to combine the contents of images taken under different diagnostic modes. Multimodal medical image fusion techniques enable complementary information acquired by different imaging devices to be automatically combined into a unique image. METHODS: In this paper, multimodal medical images fusion method based on multiresolution analysis (MRA) is proposed, with the aim to combine the high geometric content of magnetic resonance imaging (MRI) and the elasticity information of magnetic resonance elastography (MRE), simultaneously acquired on the same organs of a patient. First, the slices of MRE are volumetrically interpolated to exactly overlap, each with a slice of MRI. Then, the spatial details of MRI are extracted by means of MRA and injected into the corresponding slices of MRE. Due to the intrinsic dissimilarity between corresponding slices of MRE and MRI, the spatial details of MRI are modulated by local or global matching functions. RESULTS: The performance of the proposed method is quantitatively assessed considering radiometric and geometric consistency of the fused images with respect to their originals, in a comparison with two popular methods from the literature. For a qualitative evaluation, a visual inspection is carried out. CONCLUSIONS: The results show that the proposed method enables an effective MRI-MRE fusion that allows the elasticity information and geometric details of the examined organs to be evaluated in a single image.

Road Surface Anomaly Assessment Using Low-Cost Accelerometers: A Machine Learning Approach.

Roads are a strategic asset of a country and are of great importance for the movement of passengers and goods. Increasing traffic volume and load, together with the aging of roads, creates various types of anomalies on the road surface. This work proposes a low-cost system for real-time screening of road pavement conditions. Acceleration signals provided by on-car sensors are processed in the time-frequency domain in order to extract information about the condition of the road surface. More specifically, a short-time Fourier transform is used, and significant features, such as the coefficient of variation and the entropy computed over the energy of segments of the signal, are exploited to distinguish between well-localized pavement distresses caused by potholes and manhole covers and spread distress due to fatigue cracking and rutting. The extracted features are fed to supervised machine learning classifiers in order to distinguish the pavement distresses. System performance is assessed using real data, collected by sensors located on the car's dashboard and floorboard and manually labeled. The experimental results show that the proposed system is effective at detecting the presence and the type of distress with high classification rates.

Volumetric interpolation of tomographic sequences for accurate 3D reconstruction of anatomical parts.

BACKGROUND AND OBJECTIVE: Tomographic sequences of biomedical images are commonly used to achieve a three-dimensional visualization of the human anatomy. In some cases, the number of images contained in the sequence is limited, e.g., in low-dose computed tomography acquired on neonatal patients, resulting in a coarse and inaccurate 3D reconstruction. METHODS: In this paper, volumetric image interpolation methods, devised to increase the axial resolution of tomographic sequences and achieve a refined 3D reconstruction, are proposed and compared. The techniques taken into consideration are based on motion-compensated frame-interpolation concepts, which have been developed for video applications, mainly frame-rate conversion. RESULTS: The performance of the proposed methods is quantitatively assessed by using sequences with a simulated low axial resolution obtained from the decimation of standard high-resolution computed tomography sequences. Real data with an actual low axial resolution have been used as well for a qualitative evaluation of the proposed methods. CONCLUSIONS: The experimental results demonstrate that the proposed methods enable an effective slice interpolation and that the achievable 3D models clearly benefit from the increased axial resolution.

A Survey of Deep Learning-Based Source Image Forensics.

Image source forensics is widely considered as one of the most effective ways to verify in a blind way digital image authenticity and integrity. In the last few years, many researchers have applied data-driven approaches to this task, inspired by the excellent performance obtained by those techniques on computer vision problems. In this survey, we present the most important data-driven algorithms that deal with the problem of image source forensics. To make order in this vast field, we have divided the area in five sub-topics: source camera identification, recaptured image forensic, computer graphics (CG) image forensic, GAN-generated image detection, and source social network identification. Moreover, we have included the works on anti-forensics and counter anti-forensics. For each of these tasks, we have highlighted advantages and limitations of the methods currently proposed in this promising and rich research field.

Comments on "A new algorithm for border description of polarized light surface microscopic images of pigmented skin lesions".

In this paper, discrepancies and reference inaccuracies in the paper (Grana et al., 2003) are pointed out. Specifically, it is demonstrated that the definitions of "lesion gradient" and "skin lesion gradient," widely used in a number of medical papers on computer analysis of pigmented skin lesions, are unambiguous, and that the "new algorithm for border description" described in the subject paper substantially relies on well-established concepts dating back over one decade ago.

Multiresolution MAP despeckling of SAR images based on locally adaptive generalized Gaussian pdf modeling.

In this paper, a new despeckling method based on undecimated wavelet decomposition and maximum a posteriori MIAP) estimation is proposed. Such a method relies on the assumption that the probability density function (pdf) of each wavelet coefficient is generalized Gaussian (GG). The major novelty of the proposed approach is that the parameters of the GG pdf are taken to be space-varying within each wavelet frame. Thus, they may be adjusted to spatial image context, not only to scale and orientation. Since the MAP equation to be solved is a function of the parameters of the assumed pdf model, the variance and shape factor of the GG function are derived from the theoretical moments, which depend on the moments and joint moments of the observed noisy signal and on the statistics of speckle. The solution of the MAP equation yields the MAP estimate of the wavelet coefficients of the noise-free image. The restored SAR image is synthesized from such coefficients. Experimental results, carried out on both synthetic speckled images and true SAR images, demonstrate that MAP filtering can be successfully applied to SAR images represented in the shift-invariant wavelet domain, without resorting to a logarithmic transformation.