Segmentation of breast ultrasound images using neural networks

Ahmed A. Othman, Hamid R. Tizhoosh

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Medical image segmentation is considered a very important task for diagnostic and treatment-planning purposes. Accurate segmentation of medical images helps clinicians to clarify the type of the disease and facilitates the process of efficient treatment. In this paper, we propose two different approaches to segment breast ultrasound images using neural networks. In the first approach, we use scale invariant feature transform (SIFT) to calculate a set of descriptors for a set of points inside the image. These descriptors are used to train a supervised neural network. In the second approach, we use SIFT to detect a set of key points inside the image. Texture features are then extracted from a region around each point to train the network. This process is repeated multiple times to verify the generalization ability of the network. The average segmentation accuracy is calculated by comparing every segmented image with corresponding gold standard images marked by an expert.

Original languageEnglish (US)
Title of host publicationEngineering Applications of Neural Networks - 12th INNS EANN-SIG International Conference, EANN 2011 and 7th IFIP WG 12.5 International Conference, AIAI 2011, Proceedings
PublisherSpringer New York LLC
Number of pages10
EditionPART 1
ISBN (Print)9783642239564
StatePublished - 2011
Event12th INNS EANN-SIG International Conference on Engineering Applications of Neural Networks, EANN 2011 - Corfu, Greece
Duration: Sep 15 2011Sep 18 2011

Publication series

NameIFIP Advances in Information and Communication Technology
NumberPART 1
Volume363 AICT
ISSN (Print)1868-4238


Conference12th INNS EANN-SIG International Conference on Engineering Applications of Neural Networks, EANN 2011

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management


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