Images Sub-segmentation by Fuzzy and Possibilistic Clustering Algorithm

Benjamin Ojeda Magaña, Joel Quintanilla Domínguez, Ruben Ruelas, Jose Miguel Barrón Adame, Luis Enrique Barboza Niño, Rafael Guzmán Cabrera, Diego Andina


In this work, an alternative new methodology to segment regions in an image is proposed. The method takes the advantages offered by hybrid algorithms, as the Possibilistic Fuzzy c-Means (PFCM) clustering algorithm, which has the qualities of both the Fuzzy c-Means (FCM) and the Possibilistic c-Means (PCM). The method is called sub-segmentation, and it consists of finding some clusters in an image through the segmentation of the image and, within these clusters, the less representative pixels or atypical pixels. These elements very frequently represent the zones of interest during image analysis. Three different cases are used in order to illustrate the method. The first one is an image of a drop of milk, where the generality of the method is tested in a simple but representative image. The second case corresponds to digital mammograms, where the potentiality of the method is tested in a critical application, such as anomalies identification in mammograms for cancer detection. The last case gives an idea of its range of applications, as the method is applied to an industrial case of classification of wood boards according to their quality. As can be seen from the three cases used in this work, the results are very interesting and promising.


Pattern recognition, image processing, fuzzy clustering, possibilistic clustering, fault detection and diagnosis.

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