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Padma priya, K.
- Vertebral Fracture Assessment and Classification using Imaging Techniques – An Overview
Authors
1 Department of Electrical Engineering in Annamalai University, IN
2 Department of Electronics and Instrumentation Engineering in Annamalai University, IN
Source
Biometrics and Bioinformatics, Vol 5, No 1 (2013), Pagination: 7-13Abstract
Vertebral fracture is the most common type of fragility fracture, even these are not clinically apparent. It is now recognized that they are associated with increased risk of future fracture, increased morbidity, and increased mortality. Vertebral fractures are common osteoporotic fractures, but current quantitative detection methods (morphometry) lack specificity. Vertebral fracture assessment is a method for imaging the spine by dual-energy X-ray absorptiometry to diagnose vertebral fractures. Vertebral fracture assessment exposes the patient to less radiation than conventional spine radiographs, with lower cost and greater convenience. Vertebrae were first classified by radiologists for vertebral fractures and differentiate them from other causes of vertebral deformities. The mild deformities are diagnosed. Fractured vertebrae were graded. AAM was used to provide a semi-automatic segmentation. The resulting segmentations were used for the classification algorithm. Classifier‟s measure of the fracture likelihood estimate was found. Statistical comparisons were done between the classifiers.Keywords
Active Appearance Model (AAM), Bone Mineral Density, Computer-Assisted Diagnosis, Dual-Energy X-Ray Absorptiometry (DXA), Osteoporosis, Semi Quantitative Assessment, Vertebral Fracture Assessment (VFA).
- Low Cost Portable Embedded Face Recognition System Based on ARM9 Processor
Authors
1 Department of E.C.E., A.I.T.S, Rajampet, IN
Source
Biometrics and Bioinformatics, Vol 4, No 11 (2012), Pagination: 493-496Abstract
Human face recognition is a widely used biological recognition technology, and a core issue of safeguard. The system is based on MagicARM2440 development board, including transplantation of Linux operating system, the development of drivers, detecting face by using face class Haar feature, and then recognizing face by using Gabor features. In the paper, a face detection and recognition system (FDRS) based on video sequences and still image is proposed. It uses the AdaBoost algorithm to detect human face in the image or frame, adopts Gabor wavelet Transforms for feature extraction and recognition in face image. The related technologies are firstly outlined. Then, the system requirements and UML use case diagram are described. In addition, the paper mainly introduces the design solution and key procedures. The FDRS's source-code is built in C++ and Intel Open Source Computer Vision Library (OpenCV). Finally Haar Transform based face recognition system implemented on ARM9 processor based hardware system. The system uses USB based camera for image acquisition and operated with Linux operating system.