Segmentation of Breast Ultrasound Images Using Neural Networks

Abstract : 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.
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Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.260-269, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_30〉
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Ahmed Othman, Hamid Tizhoosh. Segmentation of Breast Ultrasound Images Using Neural Networks. Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.260-269, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_30〉. 〈hal-01571364〉

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