Traumatic Dorsal Hand Reconstruction through A free Anterolateral Thigh Flap, Induced Membrane Technique, Toe Joint Transfer, and Joint Prosthesis

Introduction:This study presents a case of post-traumatic dorsal hand reconstruction by describing the surgical technique in several stages and the outcome. Case report:It involves a patient with loss of cutaneous tissue, loss of bone, and tendon in the dorsal hand and fingers following a car accident. He was treated on a four-stage hand salvage and reconstruction. Stage one fulfilled in emergency involved K-wire and osseous filling through acrylic cement, hunter tendon rods, and a free anterolateral thigh flap. The second stage at 2 months involved osseous grafts and finger joint prostheses. The third stage time at 7 months involved a toe joint transfer. The last stage at 11 months involved extensor tendons graft reconstruction. The functional outcome at 2 years is acceptable. Conclusion:The post-traumatic dorsal hand reconstruction requires several techniques to reconstruct the losses of substances observed and this in several stages. It allowed to have an acceptable hand function. Keywords:Hand injuries, induced membrane, finger joint prosthesis, tendon transfer.

Dorsal Hand Vein Image Enhancement Using Fusion of CLAHE and Fuzzy Adaptive Gamma

Enhancement of captured hand vein images is essential for a number of purposes, such as accurate biometric identification and ease of medical intravenous access. This paper presents an improved hand vein image enhancement technique based on weighted average fusion of contrast limited adaptive histogram equalization (CLAHE) and fuzzy adaptive gamma (FAG). The proposed technique is applied using three stages. Firstly, grey level intensities with CLAHE are locally applied to image pixels for contrast enhancement. Secondly, the grey level intensities are then globally transformed into membership planes and modified with FAG operator for the same purposes. Finally, the resultant images from CLAHE and FAG are fused using improved weighted averaging methods for clearer vein patterns. Then, matched filter with first-order derivative Gaussian (MF-FODG) is employed to segment vein patterns. The proposed technique was tested on self-acquired dorsal hand vein images as well as images from the SUAS databases. The performance of the proposed technique is compared with various other image enhancement techniques based on mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM). The proposed enhancement technique’s impact on the segmentation process has also been evaluated using sensitivity, accuracy, and dice coefficient. The experimental results show that the proposed enhancement technique can significantly enhance the hand vein patterns and improve the detection of dorsal hand veins.

Invasive Microsporum canis infection as a presenting complaint of late-diagnosed HIV infection

A 43-year-old male barber presented with 1 year history of a painful, itchy plaque on the dorsal hand. Microsporum canis was recovered from pus of the lesion. Serologic testing for human immunodeficiency virus (HIV) antibody was positive, with a CD4+ count of 81 cells per cubic millimeter. Invasive cutaneous Microsporum canis infection is uncommon and can be suggestive of HIV infection or other conditions of immunocompromise.

Intelligent Technique for Human Authentication using Fusion of Finger and Dorsal Hand Veins

Multimodal biometric systems have been widely used to achieve high recognition accuracy. This paper presents a new multimodal biometric system using intelligent technique to authenticate human by fusion of finger and dorsal hand veins pattern. We developed an image analysis technique to extract region of interest (ROI) from finger and dorsal hand veins image. After extracting ROI we design a sequence of preprocessing steps to improve finger and dorsal hand veins images using Median filter, Wiener filter and Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance vein image. Our smart technique is based on the following intelligent algorithms, namely; principal component analysis (PCA) algorithm for feature extraction and k-Nearest Neighbors (K-NN) classifier for matching operation. The database chosen was the Shandong University Machine Learning and Applications - Homologous Multi-modal Traits (SDUMLA-HMT) and Bosphorus Hand Vein Database. The achieved result for the fusion of both biometric traits was Correct Recognition Rate (CRR) is 96.8%.