The extensor digitorum (ED) muscle of the hand originates from the lateral condyle of the humerus and splits into four tendons; each for one phalanx except the thumb. Literature reports have described multiple tendons (usually two) to each digit but in the presented study we observed four tendons to the ring finger, what is rare. During a routine dissection of the cadavers, we observed an anomalous arrangement of the ED tendon on the left hand of a 42-year-old male. The anomalous tendons to the ring finger were studied in detail, the surrounding structures were carefully delineated and the specimen was photographed. The ED muscle originated as usual from the lateral condyle of the humerus, continued downwards, passing inferiorly to the extensor retinaculum to split into individual tendons for each of the digits. There was a single tendon to the index, middle and ring finger as usual but the ring finger displayed four tendons. All the tendons attached to the phalanges were as described in anatomy textbooks. The arrangement of the anomalous tendons of ED to each of the digits is not uncommon, but existence of four tendons to the ring finger is extremely rare. The increased number of tendons to the ring finger may increase the extension component of the ring finger. Anatomical knowledge of the tendons of the extensor muscles of the hand may be also beneficial for hand surgeons performing graft operations (Fig. 2, Ref. 11). Full Text (Free, PDF) www.bmj.sk.
Mobile implementation is a current trend in biometric design. This paper proposes a new approach to palm print recognition, in which smart phones are used to capture palm print images at a distance. A touchless system was developed because of public demand for privacy and sanitation. Robust hand tracking, image enhancement, and fast computation processing algorithms are required for effective touchless and mobile-based recognition. In this project, hand tracking and the region of interest (ROI) extraction method were discussed. A sliding neighborhood operation with local histogram equalization, followed by a local adaptive thresholding or LHEAT approach, was proposed in the image enhancement stage to manage low-quality palm print images. To accelerate the recognition process, a new classifier, improved fuzzy-based k nearest centroid neighbor (IFkNCN), was implemented. By removing outliers and reducing the amount of training data, this classifier exhibited faster computation. Our experimental results demonstrate that a touchless palm print system using LHEAT and IFkNCN achieves a promising recognition rate of 98.64%.