ISSN 1054-6618, Pattern Recognition and Image Analysis, 2013, Vol. 23, No. 2, pp. 328–334. © Pleiades Publishing, Ltd., 2013. 1 1. INTRODUCTION An application specific processor for vein pattern extraction, and its application to a biometric identifi- cation system, is proposed in [1]. The conventional vein-pattern-recognition algorithm consists of an original image grab part, a preprocessing part, and a recognition part, and the last two parts take most of the processing time. The preprocessing part consists of a Gaussian low-pass filter (which works iteratively), a high-pass filter, and a modified median filter. Conse- quently the conventional algorithm [1, 2, and 4] con- sists of low pass spatial filtering for noise removal, high pass spatial filtering for emphasizing vascular patterns and thresholding. An improved vein pattern extracting algorithm is proposed in [3] which compensate the loss of vein pat- terns in the edge area, gives more enhanced and stabi- lized vein pattern information, and shows better per- formance than the existing algorithm. In [5], a direction-based vascular pattern extrac- tion algorithm based on the directional information of vascular patterns is presented for biometric applica- tions. It applies two different filters: row vascular pat- 1 The article is published in the original. tern extraction filter for abscissa vascular pattern extraction, and column vascular pattern extraction fil- ter for effective extraction of the ordinate vascular pat- terns. The combined output of both filters produces the final hand vascular patterns. Unlike the conven- tional hand vascular pattern extraction algorithm, the directional extraction approach prevents loss of the vascular pattern connectivity. In [6–7] a method for personal identification based on finger-vein patterns is presented and evaluated using line tracking starting from various positions. Local dark lines are identified and line tracking is exe- cuted by moving along the lines pixel by pixel. When a dark line is not detectable, a new tracking operation starts at another position. This procedure executes repeatedly, so the dark lines that tracked a lot of times have a great probability to be veins. An algorithm for finger vein pattern extraction in infrared images is proposed in [8]. The low contrast images, due to the light scattering effect, are enhanced and the fingerprint lines are removed using 2D discrete wavelet filtering. Kernel filtering produces multiple images by rotating the kernel in six different direc- tions, focus into the expected directions of the vein patterns. The maximum of all images is transformed into a binary image. Further improvement is achieved by a two-level morphological process: a majority filter smoothes the contours and removes some of the mis- classified isolated pixels, and a reconstruction proce- Finger Vein Segmentation in Infrared Images Using Supervised and Unsupervised Clustering Algorithms 1 M. Vlachos and E. Dermatas Department of Electrical Engineering and Computer Technology, University of Patras, Patras, Greece e-mail: mvlachos@teemail.gr Abstract—In this paper, two new methods to segment infrared images of finger in order to perform the finger vein pattern extraction task are presented. In the first, the widespread known and used K nearest neighbor (KNN) classifier, which is a very effective supervised method for clustering data sets, is used. In the second, a novel clustering algorithm named nearest neighbor clustering algorithm (NNCA), which is unsupervised and has been recently proposed for retinal vessel segmentation, is used. As feature vectors for the classification process in both cases two features are used: the multidirectional response of a matched filter and the mini- mum eigenvalue of the Hessian matrix. The response of the multidirectional filter is essential for robust clas- sification because offers a distinction between vein-like and edge-like structures while Hessian based approaches cannot offer this. The two algorithms, as the experimental results show, perform well with the NNCA has the advantage that is unsupervised and thus can be used for full automatic finger vein pattern extraction. It is also worth to note that the proposed vector, composed only of two features, is the simplest feature set which has proposed in the literature until now and results in a performance comparable with others that use a vector with much larger size (31 features). NNCA evaluated also quantitatively on a database which contains artificial images of finger and achieved the segmentation rates: 0.88 sensitivity, 0.80 specificity and 0.82 accuracy. Keywords: KNN, NNCA, Vein pattern, Hessian matrix, matched filter. DOI: 10.1134/S1054661813020168 Received March 11, 2011 APPLIED PROBLEMS