Artificial Intelligence System for Predicting Prostate Cancer Lesions from Shear Wave Elastography Measurements.

(1) Objective: To design an artificial intelligence system for prostate cancer prediction using the data obtained by shear wave elastography of the prostate, by comparing it with the histopathological exam of the prostate biopsy specimens. (2) Material and methods: We have conducted a prospective study on 356 patients undergoing transrectal ultrasound-guided prostate biopsy, for suspicion of prostate cancer. All patients were examined using bi-dimensional shear wave ultrasonography, which was followed by standard systematic transrectal prostate biopsy. The mean elasticity of each of the twelve systematic biopsy target zones was recorded and compared with the pathological examination results in all patients. The final dataset has included data from 223 patients with confirmed prostate cancer. Three machine learning classification algorithms (logistic regression, a decision tree classifier and a dense neural network) were implemented and their performance in predicting the positive lesions from the elastographic data measurements was assessed. (3) Results: The area under the curve (AUC) results were as follows: for logistic regression-0.88, for decision tree classifier-0.78 and for the dense neural network-0.94. Further use of an upsampling strategy for the training set of the neural network slightly improved its performance. Using an ensemble learning model, which combined the three machine learning models, we have obtained a final accuracy of 98%. (4) Conclusions: Bi-dimensional shear wave elastography could be very useful in predicting prostate cancer lesions, especially when it benefits from the computational power of artificial intelligence and machine learning algorithms.

Timely-Automatic Procedure for Estimating the Endocardial Limits of the Left Ventricle Assessed Echocardiographically in Clinical Practice.

In this paper, we propose an analytical rapid method to estimate the endocardial borders of the left ventricular walls on echocardiographic images for prospective clinical integration. The procedure was created as a diagnostic support tool for the clinician and it is based on the use of the anisotropic generalized Hough transform. Its application is guided by a Gabor-like filtering for the approximate delimitation of the region of interest without the need for computing further anatomical characteristics. The algorithm is applying directly a deformable template on the predetermined filtered region and therefore it is responsive and straightforward implementable. For accuracy considerations, we have employed a support vector machine classifier to determine the confidence level of the automated marking. The clinical tests were performed at the Cardiology Clinic of the County Emergency Hospital Timisoara and they improved the physicians perception in more than 50% of the cases. The report is concluded with medical discussions.

Numerical stability of spline-based Gabor-like systems.

The paper provides a theorem for the characterization of numerical stability of spline-type systems. These systems are generated through shifted copies of a given atom over a time lattice. Also, we reformulate the well known Gabor systems via modulated spline-type systems and we apply the corresponding numerical stability to these systems. The numerical stability is tested for consistency against deformations.

Realizable algorithm for approximating Hilbert-Schmidt operators via Gabor Multipliers.

In this work, we consider new computational aspects to improve the approximation of Hilbert-Schmidt operators via generalized Gabor multipliers. One aspect is to consider the approximation of the symbol of an Hilbert-Schmidt operator as L2 projection in the spline-type space associated to a Gabor multiplier. This gives the possibility to employ a selection procedure of the analysis and synthesis function, interpreted as time-frequency lag; hence, with the related algorithm it is possible to handle both underspread and overspread operators. In the numerical section, we exploit the case of approximating overspread operators having compact and smooth spreading function and discontinuous time-varying systems. For the latter, the approximation of discontinuities in the symbol is not straightforward achievable in the generalized Gabor multipliers setting. For this reason, another aspect is to further process the symbol through a Hough transform, to detect discontinuities and to smooth them using a new class of approximants. This procedure creates a bridge between features detections techniques and harmonic analysis methods and in specific cases it almost doubles the accuracy of approximation.

Constructive realizable multiresolution wavelet-like systems based on multi-windows spline-type spaces.

In this paper, we present a constructive realizable procedure for obtaining a multiresolution wavelet-like system in the framework of stability for spline-type spaces, considering only the generators that prominently intersect the spectrum of the signal to be approximated and stable under deformations through frequency shifts. The realizable system constructed in this way, can analyse jointly three different features of a signal namely time, scale and frequency. We present numerical tests and a detailed complexity analysis to compare the performances of our method against standard constructions.

Space-variant Gabor decomposition for filtering 3D medical images.

This is an experimental paper in which we introduce the possibility to analyze and to synthesize 3D medical images by using multi-variate Gabor frames with Gaussian windows. Our purpose is to apply a space-variant filter-like operation in the space-frequency domain to correct medical images corrupted by different types of acquisitions errors. The Gabor frames are constructed with Gaussian windows sampled on non-separable lattices for a better packing of the space-frequency plane. An implementable solution for 3D-Gabor frames with non-separable lattice is given and numerical tests on simulated data are presented.

Detecting proteine coding regions using a customized multi-scales splines construction.

In this experimental paper, we propose a method to detect proteins coding regions using a multiresolution wavelet-like system, constructed in multi-window splines-type spaces. The method is based on considering only the generators that prominently intersect the spectrum of the DNA (Deoxyribonucleic acid) sequence and on analysing jointly three different features namely time, scale and frequency. Numerical comparison and experimental results are presented.

Stability of Spline-Type Systems in the Abelian Case.

In this paper, the stability of translation-invariant spaces of distributions over locally compact groups is stated as boundedness of synthesis and projection operators. At first, a characterization of the stability of spline-type spaces is given, in the standard sense of the stability for shift-invariant spaces, that is, linear independence characterizes lower boundedness of the synthesis operator in Banach spaces of distributions. The constructive nature of the proof for Theorem 2 enabled us to constructively realize the biorthogonal system of a given one. Then, inspired by the multiresolution analysis and the Lax equivalence for general discretization schemes, we approached the stability of a sequence of spline-type spaces as uniform boundedness of projection operators. Through Theorem 3, we characterize stable sequences of stable spline-type spaces.

A parallel Homological Spanning Forest framework for 2D topological image analysis.

In [14], a topologically consistent framework to support parallel topological analysis and recognition for 2D digital objects was introduced. Based on this theoretical work, we focus on the problem of finding efficient algorithmic solutions for topological interrogation of a 2D digital object of interest D of a presegmented digital image I, using 4-adjacency between pixels of D. In order to maximize the degree of parallelization of the topological processes, we use as many elementary unit processing as pixels the image I has. The mathematical model underlying this framework is an appropriate extension of the classical concept of abstract cell complex: a primal-dual abstract cell complex (pACC for short). This versatile data structure encompasses the notion of Homological Spanning Forest fostered in [14,15]. Starting from a symmetric pACC associated with I, the modus operandi is to construct via combinatorial operations another asymmetric one presenting the maximal number of non-null primal elementary interactions between the cells of D. The fundamental topological tools have been transformed so as to promote an efficient parallel implementation in any parallel-oriented architecture (GPUs, multi-threaded computers, SIMD kernels and so on). A software prototype modeling such a parallel framework is built.

Approximate Duals of Gabor-like Frames Based on Realizable Multi-Window Spline-Type Constructions.

In the multi-window spline-type spaces, the fast computation of the realizable dual frame could be achieved through a constructive reformulation of the biorthogonal relations. In this paper, we extend the results obtained in spline-type spaces, for the constructive realization of an approximate dual Gabor-like frame. We demonstrate the advantages of this approach in both flexibility and speed. The method allows in a natural way to handle non standard Gabor constructions like non-uniformity in frequency and the reductions of the number of used modulations. Experimental tests are presented in support of the algorithm.

A Parallel Implementation for Computing the Region-Adjacency-Tree of a Segmentation of a 2D Digital Image.

A design and implementation of a parallel algorithm for computing the Region-Adjacency Tree of a given segmentation of a 2D digital image is given. The technique is based on a suitable distributed use of the algorithm for computing a Homological Spanning Forest (HSF) structure for each connected region of the segmentation and a classical geometric algorithm for determining inclusion between regions. The results show that this technique scales very well when executed in a multicore processor.

On Homotopy Continuation for Speech Restoration.

In this paper, a homotopy-based method is employed for the recovery of speech recordings from missing or corrupted samples taken in a noisy environment. The model for the acquisition device is a compressed sensing scenario using Gabor frames. To recover an approximation of the speech file, we used the basis pursuit denoising method with the homotopy continuation algorithm. We tested the proposed method with various speech recordings.