Francesco Rundo

Self Organizing Motor Maps for Color-Mapped Image Re-Indexing

Palette re-ordering is an effective approach for improving the compression of color-indexed images. If the spatial distribution of the indexes in the image is smooth, greater compression ratios may be obtained. As is already known, obtaining an optimal re-indexing scheme is not a trivial task. In this paper, we provide a novel algorithm for palette re-ordering problem making use of a motor map neural network. Experimental results show the real effectiveness of the proposed method both in terms of compression ratio and zero-order entropy of local differences. Also, its computational complexity is competitive with previous works in the field.

A bio-inspired CNN with re-indexing engine for lossless DNA microarray compression and segmentation

The DNA microarray images allow to analyze the natural gene expressions. In this paper we propose an advanced method to efficiently address the imaging storage as well as the performance of the algorithm used to retrieve information from DNA images. The Cellular Neural Networks (CNNs) based core is able to provide a method to extract foreground (the DNA gene expression information) from DNA images. It is also proposed an innovative method to compress the DNA image by re-organizing the signal data belonging to the background by making use of a novel way to apply the re-indexing techniques to almost “uncorrelated” signal. Experiments confirm how the proposed method outperform previous solution in almost all cases.

A Cellular Neural Network for Zooming Digital Colour Images

In this paper we propose a novel zooming algorithm based on the use of cellular neural networks (CNNs). The CNNs have been used successfully in many areas of image processing showing good performance with high speed computation capability. In this work, we propose a CNN applied to digital colour image enlargement. Moreover, an adaptive gradient-driven enhancement process is used to improve the quality of the enlarged image generated by the CNN processing. The reported experimental results show the effectiveness of the proposed algorithm.

An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment

Physiological signals are widely used to perform medical assessment for monitoring an extensive range of pathologies, usually related to cardio-vascular diseases. Among these, both PhotoPlethysmoGraphy (PPG) and Electrocardiography (ECG) signals are those more employed. PPG signals are an emerging non-invasive measurement technique used to study blood volume pulsations through the detection and analysis of the back-scattered optical radiation coming from the skin. ECG is the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin. In the present paper we propose a physiological ECG/PPG “combo” pipeline using an innovative bio-inspired nonlinear system based on a reaction-diffusion mathematical model, implemented by means of the Cellular Neural Network (CNN) methodology, to filter PPG signal by assigning a recognition score to the waveforms in the time series. The resulting “clean” PPG signal exempts from distortion and artifacts is used to validate for diagnostic purpose an EGC signal simultaneously detected for a same patient. The multisite combo PPG-ECG system proposed in this work overpasses the limitations of the state of the art in this field providing a reliable system for assessing the above-mentioned physiological parameters and their monitoring over time for robust medical assessment. The proposed system has been validated and the results confirmed the robustness of the proposed approach.

Adaptive Compression of Stereoscopic Images

Nowadays the growing availability of stereo cameras for common applications is becoming a commodity. This paper addresses the problem of stereoscopic images data compression proposing an innovative algorithm for compressing Multi Picture Object coded stereopairs. By means of self organizing reconstruction algorithm based on image redundancy we are able to reduce the size of the enclosed JPEG images. The overall perceived (and measured) quality is managed by considering that a stereoscopic image represents the same scene acquired from two different perspectives. In particular we achieve some compression gain just encoding the two images with different quality factors. The reported results and test benchmarks show the robustness and efficiency of the proposed algorithm.

Advanced Bio-Inspired System for Noninvasive Cuff-Less Blood Pressure Estimation from Physiological Signal Analysis

Blood Pressure (BP) is one of the most important physiological indicators that provides useful information in the field of health-care monitoring. Blood pressure may be measured by both invasive and non-invasive methods. A novel algorithmic approach is presented to estimate systolic and diastolic blood pressure accurately in a way that does not require any explicit user calibration, i.e., it is non-invasive and cuff-less. The approach herein described can be applied in a medical device, as well as in commercial mobile smartphones by an ad hoc developed software based on the proposed algorithm. The authors propose a system suitable for blood pressure estimation based on the PhotoPlethysmoGraphy (PPG) physiological signal sampling time-series. Photoplethysmography is a simple optical technique that can be used to detect blood volume changes in the microvascular bed of tissue. It is non-invasive since it takes measurements at the skin surface. In this paper, the authors present an easy and smart method to measure BP through careful neural and mathematical analysis of the PPG signals. The PPG data are processed with an ad hoc bio-inspired mathematical model that estimates systolic and diastolic pressure values through an innovative analysis of the collected physiological data. We compared our results with those measured using a classical cuff-based blood pressure measuring device with encouraging results of about 97% accuracy.

Oral Metronomic Vinorelbine in Advanced Non-small Cell Lung Cancer Patients Unfit for Chemotherapy

Aim: To explore the feasibility and activity of oral metronomic vinorelbine patients with advanced NSCLC not eligible to standard chemotherapy because of old age (≥70 years), and/or poor Eastern Cooperative Oncology Group performance status (≥2), and/or extensive brain or bone disease, and/or active comorbidities (≥2) requiring for pharmacological treatment. Patients and Methods: In a prospective phase II not randomized study, patients with stage IV NSCLC unfit to chemotherapy were treated with oral metronomic vinorelbine at 30 mg fixed dose three times a week until disease progression. Results: Fifty patients were treated, 19 (38%) in the first-line setting. Five patients (11%) experienced a grade 3 toxicity; no grade 4 toxicity occurred. Overall disease control rate was 32%, 44% and 26% in first and subsequent lines, respectively (p=0.39). Median OS and PFS were 7.3 months (95% confidence interval [CI]=4.7-10.0) and 2.7 months (95%CI=2.0-3.4), respectively. Conclusion: These data support the activity and safety of metronomic vinorelbine in a relevant proportion of patients usually excluded from any specific treatment.

Evaluation of Levenberg–Marquardt neural networks and stacked autoencoders clustering for skin lesion analysis, screening and follow-up

Traditional methods for early detection of melanoma rely on the visual analysis of the skin lesions performed by a dermatologist. The analysis is based on the so-called ABCDE (Asymmetry, Border irregularity, Colour variegation, Diameter, Evolution) criteria, although confirmation is obtained through biopsy performed by a pathologist. The proposed method exploits an automatic pipeline based on morphological analysis and evaluation of skin lesion dermoscopy images. Preliminary segmentation and pre-processing of dermoscopy image by SC-cellular neural networks is performed, in order to obtain ad-hoc grey-level skin lesion image that is further exploited to extract analytic innovative hand-crafted image features for oncological risks assessment. In the end, a pre-trained Levenberg–Marquardt neural network is used to perform ad-hoc clustering of such features in order to achieve an efficient nevus discrimination (benign against melanoma), as well as a numerical array to be used for follow-up rate definition and assessment. Moreover, the authors further evaluated a combination of stacked autoencoders in lieu of the Levenberg–Marquardt neural network for the clustering step.

Bio-Inspired Feed-Forward System for Skin Lesion Analysis, Screening and Follow-Up.

Traditional methods for early detection of melanoma rely upon a dermatologist who visually analyzes skin lesion using the so called ABCDE (Asymmetry, Border irregularity, Color variegation, Diameter, Evolution) criteria even though conclusive confirmation is obtained through biopsy performed by pathologist. The proposed method shows a bio-inspired feed-forward automatic pipeline based on morphological analysis and evaluation of skin lesion dermoscopy image. Preliminary segmentation and pre-processing of dermoscopy image by SC-Cellular Neural Networks is performed in order to get ad-hoc gray-level skin lesion image in which we compute analytic innovative hand-crafted image features for oncological risks assessment. At the end, pre-trained Levenberg-Marquardt Neural Network is used to perform ad-hoc clustering of such hand-crafted image features in order to get an efficient nevus discrimination (benign against melanoma) as well as a numerical array to be used for follow-up rate definition and assessment.

Progresses towards a processing pipeline in photoplethysmogram (PPG) based on SiPMs

We report progresses in the development of a processing pipeline intended for photoplethysmogram (PPG) based on the use of silicon photomultipliers (SiPMs) detectors. Such probe sensors offer relevant advantages in terms of single-photon sensitivity and high internal gain for relatively low reverse bias. Along with the SiPMs detectors, an ad-hoc processing pipeline, we developed, offers advantages e.g. in order to correct signal distortions. The processing pipeline is composed by the PPG raw signal filter, consisting of a FIR pass-band scheme (low-pass filter plus high-pass ones), the PPG pattern recognition system, and completed with the medical indicators detection system and the indicators extraction.