A new marketing segmentation approach based on marginal individual utilities: applying CRM is not a chimera anymore

In the advent of Customer Relationship Management, a more accurate profile of the consumer is needed. The objective of this paper is to show the usefulness of knowing consumer’s complete utility function through his/her marginal utilities. This approach allows one to form groups of individuals with similar preferences (as traditional segmentation methods do) and to treat them individually (which represents an advance). The empirical application is carried out, on a sample of 2,127 individuals, in the context of tourism, where the customer relationship management philosophy is gaining more and more relevance.

New framework for score segmentation and analysis in OpenMusic

We present new tools for the segmentation and analysis of musical scores in the OpenMusic computer-aided composition environment. A modular object-oriented framework enables the creation of segmentations on score objects and the implementation of automatic or semi-automatic analysis processes. The analyses can be performed and displayed thanks to customizable classes and callbacks. Concrete examples are given, in particular with the implementation of a semi-automatic harmonic analysis system and a framework for rhythmic transcription.

Automatic Abdominal Aortic Aneurysm segmentation in MR images

Abdominal Aortic Aneurism is a disease related to a weakening in the aortic wall that can cause a break in the aorta and the death. The detection of an unusual dilatation of a section of the aorta is an indicative of this disease. However, it is difficult to diagnose because it is necessary image diagnosis using computed tomography or magnetic resonance. An automatic diagnosis system would allow to analyze abdominal magnetic resonance images and to warn doctors if any anomaly is detected. We focus our research in magnetic resonance images because of the absence of ionizing radiation. Although there are proposals to identify this disease in magnetic resonance images, they need an intervention from clinicians to be precise and some of them are computationally hard. In this paper we develop a novel approach to analyze magnetic resonance abdominal images and detect the lumen and the aortic wall. The method combines different algorithms in two stages to improve the detection and the segmentation so it can be applied to similar problems with other type of images or structures. In a first stage, we use a spatial fuzzy C-means algorithm with morphological image analysis to detect and segment the lumen; and subsequently, in a second stage, we apply a graph cut algorithm to segment the aortic wall. The obtained results in the analyzed images are pretty successful obtaining an average of 79% of overlapping between the automatic segmentation provided by our method and the aortic wall identified by a medical specialist. The main impact of the proposed method is that it works in a completely automatic way with a low computational cost, which is of great significance for any expert and intelligent system.