Improved prediction-based ovarian follicle detection from a sequence of ultrasound images.
A new algorithm is presented for ovarian follicle recognition from a sequence of ultrasound images. The basic version of the prediction-based algorithm is upgraded by means of two improvements. The negative influence brought by the gross measurement errors is suppressed, and the locality of the treated process is considered. The basis for both improvements is the Kalman filter. The proposed algorithm is a combination of three mutually dependent Kalman filters: a global one whose parameters are then modified by two additional ones, firstly detecting the gross measurement errors and secondly, regarding the recognised contour of the object. The obtained results show that the follicles recognised using the final prediction algorithm are about 2% more compact and about 6% more accurate, on average, when compared to the values obtained using the basic prediction-based algorithm.
Potocnik
Bozidar
B
Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia. bozo.potocnik@uni-mb.si
Zazula
Damjan
D
D000465:Algorithms; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008432:Mathematical Computing; D006080:Ovarian Follicle / Q000000981:diagnostic imaging*; D014463:Ultrasonography