Uniform Local Binary Pattern Features Research Articles

Finger vein biometric verification using block multi-scale uniform local binary pattern features and block two-directional two-dimension principal component analysis

Finger vein recognition has drawn increasing attention as one of the most popular and promising biometrics due to its high distinguishes ability, security and non-invasive procedure. The focus of this paper is on proposing new schemes based on finger vein patterns by employing block uniform local binary pattern and block two-directional two-dimension principal component analysis method in order to effectively reduce data redundancy. First, a block multi-scale uniform local binary pattern (MULBP) features operator based on improved circular neighborhood is employed to extract the local texture features of finger vein images effectively. Then two-directional two-dimension principal component analysis ((2D)2PCA) method is applied to finger vein recognition in this paper, which can effectively reduce the dimension of feature matrix and improve system performance. Furthermore, in order to avoid the disadvantage of (2D)2PCA method which cannot preserve some important local features, this paper adopts (2D)2PCA method based on block to preserve local information of image. The experimental results revealed that our proposed method achieved superior performance for the FV-USM finger vein database with a recognition rate of99.32 % and consistent performance for the Tianjin finger vein database with a recognition rate more than 99 %. Above results show that this new algorithm is effective and feasible in finger vein recognition system.

Improved Deep Learning Network Based in combination with Cost-sensitive Learning for Early Detection of Ovarian Cancer in Color Ultrasound Detecting System.

With the development of theories and technologies in medical imaging, most of the tumors can be detected in the early stage. However, the nature of ovarian cysts lacks accurate judgement, leading to that many patients with benign nodules still need Fine Needle Aspiration (FNA) biopsies or surgeries, increasing the physical pain and mental pressure of patients as well as unnecessary medical health care costs. Therefore, we present an image diagnosis system for classifying the ovarian cysts in color ultrasound images, which novelly applies the image features fused by both high-level features from deep learning network and low-level features from texture descriptor. Firstly, the ultrasound images are enhanced to improve the quality of training data set and the rotation invariant uniform local binary pattern (ULBP) features are extracted from each of the images as the low-level texture features. Then the high-level deep features extracted by the fine-tuned GoogLeNet neural network and the low-level ULBP features are normalized and cascaded as one fusion feature that can represent both the semantic context and the texture patterns distributed in the image. Finally, the fusion features are input to the Cost-sensitive Random Forest classifier to classify the images into "malignant" and "benign". The high-level features extracted by the deep neural network from the medical ultrasound image can reflect the visual features of the lesion region, while the low-level texture features can describe the edges, direction and distribution of intensities. Experimental results indicate that the combination of the two types of features can describe the differences between the lesion regions and other regions, and the differences between lesions regions of malignant and benign ovarian cysts.