Abstract
BackgroundThis 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.MethodsThe 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.ResultsThe 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.ConclusionsWe 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.
Highlights
This work investigates the applicability of a novel clustering approach to the segmentation of mammographic digital images
The most widely used method for detecting breast cancer in its early stages is the mammography, a technique which has lately taken advantage of the supplementary features offered by the digital format [1]
One of the generic segmentation approaches proposed more than three decades ago is the feature-based clustering method [21], which associates to each pixel or group of pixels from the image a set of appropriately chosen numerical parameters and transforms the primary segmentation task in a derived clustering problem in the associated feature space
Summary
This work investigates the applicability of a novel clustering approach to the segmentation of mammographic digital images. One of the generic segmentation approaches proposed more than three decades ago is the feature-based clustering method [21], which associates to each pixel or group of pixels from the image a set of appropriately chosen numerical parameters and transforms the primary segmentation task in a derived clustering problem in the associated feature space. Within this approach, the process of feature clustering becomes the crucial part of the segmentation algorithm.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
