Automated analysis of a sequence of ovarian ultrasound images. Part II: prediction-based object recognition from a sequence of images
Part I of this paper introduced a new algorithm for object detection on a single static image from an image sequence. Part II extends this basic 2D recognition scheme by incorporating knowledge about previous image recognition. A new algorithm is presented for object recognition from an image sequence using prediction procedures. It is based on the Kalman filter (KF). The measurement system is realised with an algorithm for static 2D images. An object model is set based on measurements in the first image of sequence. This model is modified from image to image using the KF in regard to new measurements. The calculation for a particular image defines a new best estimate of the object searched for. This prediction algorithm (PA) was tested on sequences of ovarian ultrasound images with follicles. The obtained results are much more compact and accurate using the PA than with the 2D algorithm only (up to 30% according to the initial values). The number of misidentified follicles is considerably lower (up to 75%).
ovarian ultrasound images prediction image sequence kalman filter object tracking