In the automatic detection framework, there have been many attempts to develop models for real-time melanoma detection. To effectively discriminate benign and malign skin lesions, this work investigates sixty different architectures of the Feedforward Back Propagation Network (FFBPN), based on shape asymmetry for an optimal structural design that includes both the hidden neuron number and the input data selection. The reason for the choice of shape asymmetry was based on the 5–10% disagreement between dermatologists regarding the efficacy of asymmetry in the diagnosis of malignant melanoma. Asymmetry is quantified based on lesion shape (contour), moment of inertia of the lesion shape and histograms. The FFBPN has a high architecture flexibility, which indicates it as a favorable tool to avoid the over-parameterization of the ANN and, equally, to discard those redundant input datasets that usually result in poor test performance. The FFBPN was tested on four public image datasets containing melanoma, dysplastic nevus and nevus images. Experimental results on multiple benchmark data sets demonstrate that asymmetry A2 is a meaningful feature for skin lesion classification, and FFBPN with 16 neurons in the hidden layer can model the data without compromising prediction accuracy.

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The ship resistance and powering are the most important hydrodynamics performances. In the case of a fishing vessel, both the design speed and trawling speed conditions must be analysed. The ship resistance and powering prediction at design speed were performed in this paper, on the basis of the Holtrop-Mennen method, by using two specific CAD-CAE platforms: AVEVA MARINE (Initial Design module) and PHP (developed in the Naval Architecture Faculty of “Dunărea de Jos” University of Galati). A comparative analysis of the theoretical prediction of the fishing vessel resistance, at full loading condition, with experimental results obtained at the model resistance tests, was presented and the level of accuracy was evaluated. Significant differences were determined between theoretical and experimental results. Also, the trawling condition was analysed. The trawl resistance was estimated on the basis of the typical empirical relations and the simulation software Trawl Vision Software (TVS), developed by AcruxSoft. A considerable increase of the trawl resistance with the ship speed was observed. The trawling speed was calculated on the basis of the brake power necessary for the design speed condition. Taking into account both the ship resistance and trawl resistance estimation, an increase in the accuracy level of the theoretical methods must be performed. Also, numerical and experimental researches must be developed related to the influence of the typical geometry of the trawl, on the resistance performance.

MR image is SNR because it is slightly tissue-depended. From the hybrid metrics, the most used is SSIM. CONCLUSION: This paper summarized the objective and hybrids metrics that are Human Vision System-based characteristics. Also, it discusses on the notion of image quality assessment. The problems faced by various metrics are highlighted and the advantage of utilized a certain metric or a tandem of certain metrics are emphasized.

Abstract. Given a grey-intensity image, our method detects the optimal threshold for a suitable binarization of MR brain images. In MR brain image processing, the grey levels of pixels belonging to the object are not substantially different from the grey levels belonging to the background. Threshold optimization is an effective tool to separate objects from the background and further, in classification applications. This paper gives a detailed investigation on the selection of thresholds. Our method does not use the well-known method for binarization. Instead, we perform a simple threshold optimization which, in turn, will allow the best classification of the analyzed images into healthy and multiple sclerosis disease. The dissimilarity (or the distance between classes) has been established using the clustering method based on dendrograms. We tested our method using two classes of images: the first consists of 20 T2-weighted and 20 proton density PD-weighted scans from two healthy subjects and from two patients with multiple sclerosis. For each image and for each threshold, the number of the white pixels (or the area of white objects in binary image) has been determined. These pixel numbers represent the objects in clustering operation. The following optimum threshold values are obtained, T = 80 for PD images and T = 30 for T2w images. Each mentioned threshold separate clearly the clusters that belonging of the studied groups, healthy patient and multiple sclerosis disease.