✦ LIBER ✦

Computerized detection and recognition of follicles in ovarian ultrasound images: a review

✍ Scribed by Potočnik, Božidar; Cigale, Boris; Zazula, Damjan

Book ID: 118814865
Publisher: Springer
Year: 2012
Tongue: English
Weight: 333 KB
Volume: 50
Category: Article
ISSN: 1741-0444
DOI: 10.1007/s11517-012-0956-y

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Improved prediction-based ovarian follic

Improved prediction-based ovarian follicle detection from a sequence of ultrasound images

✍ Božidar Potočnik; Damjan Zazula 📂 Article 📅 2003 🏛 Elsevier Science 🌐 English ⚖ 363 KB

Ultrasound image characteristics of ovar

Ultrasound image characteristics of ovarian follicles in relation to oocyte competence and follicular status in cattle

✍ R Vassena; G.P Adams; R.J Mapletoft; R.A Pierson; J Singh 📂 Article 📅 2003 🏛 Elsevier Science 🌐 English ⚖ 219 KB

Aetiology and pathogenesis of cystic ova

Aetiology and pathogenesis of cystic ovarian follicles in dairy cattle: a review

✍ Vanholder, Tom; Opsomer, Geert; de Kruif, Aart 📂 Article 📅 2006 🏛 Elsevier BV 🌐 French ⚖ 199 KB

A comparative study of the accuracy of u

A comparative study of the accuracy of ultrasound imaging, X-ray computerized tomography and low field MRI diagnosis of ovarian malignancy

✍ F.W. Smith; G.R. Cherryman; A.P. Bayliss; W.T. Fullerton; A.N.R. Law; E.M. Rober 📂 Article 📅 1988 🏛 Elsevier Science 🌐 English ⚖ 369 KB

Alpha fetoprotein, ultrasound, computeri

Alpha fetoprotein, ultrasound, computerized tomography and magnetic resonance imaging for detection of hepatocellular carcinoma in patients with advanced cirrhosis

✍ N. SNOWBERGER; S. CHINNAKOTLA; R. M. LEPE; J. PEATTIE; R. GOLDSTEIN; G. B. KLIN 📂 Article 📅 2007 🏛 John Wiley and Sons 🌐 English ⚖ 85 KB

A review of automatic mass detection and

A review of automatic mass detection and segmentation in mammographic images

✍ Arnau Oliver; Jordi Freixenet; Joan Martí; Elsa Pérez; Josep Pont; Erika R.E. De 📂 Article 📅 2010 🏛 Elsevier Science 🌐 English ⚖ 788 KB

The aim of this paper is to review existing approaches to the automatic detection and segmentation of masses in mammographic images, highlighting the key-points and main differences between the used strategies. The key objective is to point out the advantages and disadvantages of the various approac