The degree of spiculation of the tumor edge is a particularly relevant indicator of malignancy in the analysis of breast tumoral masses. This paper introduces four new methods for extracting the spiculation feature of a detected breast lesion on mammography by segmenting the contour of the lesion in a number of regions which are separately analysed, determining a characterizing spiculation feature set. In order to differentiate between benign and malignant tumors based on the extracted spiculation sets, an intelligent neural network is first trained on a number of 96 cases of known breast cancer malignancy and then tested for diagnosing and classifying breast cancer tumors. The input of the neural network is thus the extracted spiculation feature set and the output is represented by the histopatological diagnostic given by doctors. Finally, the performance of the introduced methods is analysed depending on the number of regions in which the contour is segmented and the performance-related conclusions are stated for each of the methods. The highlight of this paper is the division of the tumour contour in regions and the assessment of a spiculation indicator for each region, resulting a set of spiculation indicators that characterise the tumour and -by training a neural network -can be used in classifying breast tumours with high performance.

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Continuous vibration monitoring of mechanical roller bearing parts potentially reduces machine downtime through timely prediction and diagnosis of abnormal events. Despite the progress made in the literature, challenges remain in how to assess performance related information for maintenance decision-making from large data streams. Furthermore, since roller bearings operate under various regimes (e.g., speed and load), it is not trivial to consider the effect of regime changes in the modeling in order to reduce false alarms. The paper describes a multi-model approach to monitor the condition of roller bearings under different operating regimes. Two modeling approaches for anomaly and degradation monitoring are proposed to automatically retrieve information from the data. A self-organizing map (SOM) and a support vector machines (SVM) are used comparatively for the evaluation of a bearing degradation in time (i.e., a dynamic health indicator) and for the determination of changes in the tracked features. The proposed method is validated using data from multiple bearings of the same type.