Abstract
This paper presents a novel method for the automatic detection of microcalcifications in regions of interest images. Automatic detection method is implemented by feature extraction and sub-segmentation steps. The feature extraction step is improved using a top-hat transform such that microcalcifications can be highlighted. In a second step a sub-segmentation method based on the possibilistic fuzzy c-means clustering algorithm is applied in order to segment the images and as a way to identify the atypical pixels inside the regions of interest as the pixels representing microcalcifications. Once the pixels representing these objects have been identified, an artificial neural network model is used to learn the relations between atypical pixels and microcalcifications, such that the model can be used for aid diagnosis, and a medical could determine if these regions of interest are benign or malignant. So, as the results show, the proposed approach is a good alternative for the detection of suspicious regions,...
Sign-in/Register to access full text options 
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 callMore From: International Journal of Intelligent Computing in Medical Sciences & Image Processing
Disclaimer: 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.
