Automated algorithm for ovarian cysts detection in ultrasonogram

Author

Rihana, Sandy ; Moussallem, Hares ; Skaf, Chiraz ; Yaacoub, Charles

Author_Institution

Biomed. Eng. Dept., Holy Spirit Univ. of Kaslik (USEK), Jounieh, Lebanon

fYear

2013

fDate

11-13 Sept. 2013

Firstpage

219

Lastpage

222

Abstract

Polycystic Ovary Syndrome (PCOS) is a female endocrine disorder which severely affects women´s health and its diagnostic requires medical treatment or even surgery. Manual analysis of PCOS diagnosis often produces errors. Recently, many automated algorithms have been studied for polycysts detection in Ultrasound images. This paper presents cysts detection and classification in the ovary ultrasound images with an accuracy that reaches 90%.

Keywords

biomedical ultrasonics; image classification; medical disorders; medical image processing; surgery; ultrasonic therapy; PCOS diagnosis; female endocrine disorder; medical treatment; ovarian cysts detection; polycystic ovary syndrome; polycysts detection; surgery; ultrasonogram; ultrasound image classification; women health; Accuracy; Biomedical imaging; Feature extraction; Image segmentation; Shape; Standards; Ultrasonic imaging; cysts; multiscale morphological method; svm; thresholding; ultrasound medical imaging;

fLanguage

English

Publisher

ieee

Conference_Titel

Advances in Biomedical Engineering (ICABME), 2013 2nd International Conference on

Conference_Location

Tripoli

Print_ISBN

978-1-4799-0249-1

Type

conf

DOI

10.1109/ICABME.2013.6648887

Filename

6648887

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1957681