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Open Access Highly Accessed Research article

Mammographic images segmentation based on chaotic map clustering algorithm

Marius Iacomi12, Donato Cascio1*, Francesco Fauci1 and Giuseppe Raso1

Author Affiliations

1 Dipartimento di Fisica e Chimica, Università Degli Studi di Palermo, Palermo, Italy

2 Institutul de Ştiinţe Spaţiale, Bucharest, Măgurele, Romania

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BMC Medical Imaging 2014, 14:12  doi:10.1186/1471-2342-14-12

Published: 25 March 2014



This work investigates the applicability of a novel clustering approach to the segmentation of mammographic digital images. The chaotic map clustering algorithm is used to group together similar subsets of image pixels resulting in a medically meaningful partition of the mammography.


The image is divided into pixels subsets characterized by a set of conveniently chosen features and each of the corresponding points in the feature space is associated to a map. A mutual coupling strength between the maps depending on the associated distance between feature space points is subsequently introduced. On the system of maps, the simulated evolution through chaotic dynamics leads to its natural partitioning, which corresponds to a particular segmentation scheme of the initial mammographic image.


The system provides a high recognition rate for small mass lesions (about 94% correctly segmented inside the breast) and the reproduction of the shape of regions with denser micro-calcifications in about 2/3 of the cases, while being less effective on identification of larger mass lesions.


We can summarize our analysis by asserting that due to the particularities of the mammographic images, the chaotic map clustering algorithm should not be used as the sole method of segmentation. It is rather the joint use of this method along with other segmentation techniques that could be successfully used for increasing the segmentation performance and for providing extra information for the subsequent analysis stages such as the classification of the segmented ROI.

Chaotic maps; Clustering algorithms; Cooperative behavior; Segmentation; Mammography; Features; Mass lesions; Microcalcifications; Breast cancer