Segmentation of Breast Ultrasound Images Using Neural Networks - Engineering Applications of Neural Networks - Part I Access content directly
Conference Papers Year : 2011

Segmentation of Breast Ultrasound Images Using Neural Networks

Ahmed A. Othman
  • Function : Author
  • PersonId : 1014113
Hamid R. Tizhoosh
  • Function : Author
  • PersonId : 1014114


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.
Fichier principal
Vignette du fichier
978-3-642-23957-1_30_Chapter.pdf (224.26 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01571364 , version 1 (02-08-2017)





Ahmed A. Othman, Hamid R. Tizhoosh. Segmentation of Breast Ultrasound Images Using Neural Networks. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. pp.260-269, ⟨10.1007/978-3-642-23957-1_30⟩. ⟨hal-01571364⟩
109 View
106 Download



Gmail Facebook X LinkedIn More