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Leukocyte segmentation and SVM classification in blood smear images

Ramoser H.

Machine Graphics and Vision

2008

Vol. 17, No. 1/2

187-200

Automated leukocyte detection, segmentation, and classification is an important task in clinical diagnosis. In this paper we present an approach to leukocyte cytoplasm and nucleus segmentation that is robust with respect to image quality and cell appearance. Cell properties are described by a set of statistical color and shape features. Pairwise coupling of SVM classification results is used to determine cell type probabilities. Evaluation of the method on a set of 1166 images containing 13 different cell types has resulted in 95% correctly segmented cells and a classification accuracy of 88% (at 20% reject rate).

Computationally efficient and reliable fingerprint mosaicking on embedded hardware using minutiae

Ramoser H., Birchbauer J., Bischof H.

Machine Graphics and Vision

2004

Vol. 13, No. 4

401-415

Fingerprint matching is a common technique for biometric authentication. Solid state sensors allow the use of fingerprint recognition in small sized embedded systems. The size of these sensors makes it necessary to store several impressions of the same finger to provide good coverage of the entire fingertip. In order to reduce memory requirements and matching time all these impressions can be fused into a single larger image. Memory constraints imposed by embedded computers prohibit the use of images. A fingerprint is therefore represented as a set of minutiae coordinates and minutiae angles. We present a two stage approach to combine two fingerprints. First, a RANSAC based method is used to determine a rigid transformation which roughly aligns the two fingerprints. Second, the transformation is optimised using a robust least median of squares solution. The reliability of the method is demonstrated on a large synthetic dataset and real fingerprint images. The computational complexity and memory requirements allow implementation of the algorithm on embedded hardware.