|Title:||Computer-aided diagnosis for B-mode, elastography and automated breast ultrasound||Authors:||Chang, R.-F.
|Issue Date:||2014||Journal Volume:||8539 LNCS||Start page/Pages:||9-15||Source:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)||Abstract:||
This review paper encapsulates the presentation of the computer-aided diagnosis (CAD) development in the session of US imaging at IWDM 2014. The development includes novel methodologies in conventional B-mode and modern ultrasound modalities such as elastography and automated breast ultrasound. For B-mode images, gray-scale invariant texture features were proposed to solve the changing of echogenicities from various ultrasound systems. Speckle patterns were analyzed to show the properties of tiny scatterers with microstructure contained in breast tissues for tissue characterization. Using quantified sonographic findings in tumor classification can achieve better diagnostic result than combining all features together. Elastography CAD systems use automatic tumor segmentation and clustering method to reduce operator-dependence. Dynamic sequence features were extracted from a sequence of elastograms to provide tumor stiffness without selecting slices. Another approach was selecting slices with quality evaluation methods. Both approaches reduced the overloads of physicians in slice selection. Automated breast ultrasound system is developed to automatically scan the whole breast and build the volumetric breast structure. Three-dimensional morphology, texture, and speckle features were proposed and combined to provide more diagnostic information than two-dimensional features. These CAD systems for B-mode, elastography, and automated breast ultrasound are good at malignancy evaluation and would be helpful in clinic use. ? 2014 Springer International Publishing.
|URI:||https://scholars.lib.ntu.edu.tw/handle/123456789/489585||DOI:||10.1007/978-3-319-07887-8_2||metadata.dc.subject.other:||Automation; Cluster analysis; Computer aided diagnosis; Quality control; Speckle; Textures; Tissue engineering; Tumors; Ultrasonic equipment; Ultrasonics; Breast Cancer; Invariant texture features; Quality evaluation method; Sonographic findings; Three dimensional morphology; Tissue characterization; Tumor classification; Two-dimensional features; Medical imaging
|Appears in Collections:||資訊工程學系|
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