Characterization of bone Tissue by microwaves using wavelets and KNN

Barros, Jannayna Domingues; Júnior, José Josemar de Oliveira; Silva, Sandro Gonçalves da; Farias, Robson Fernandes de

doi:10.1590/s2179-10742011000100020

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…Approximation coefficients will include low frequency components and detailed coefficients will include high frequency components. Wavelet transform gets the input image and transform into multiple frequency bands [10]. The standard wavelet mostly used in image feature extraction is daubechies wavelet.…”

Section: Fig 4 : Wavelet Decompositionmentioning

confidence: 99%

Computer Aided Analysis of Lung Ct Based on Transform Domain Features

Minnal

2014

IJCSI

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As Many CADx systems have been developed to detect lung cancer based on spatial domain features that process only the pixel intensity values, the proposed scheme applies frequency transform to the lung images to extract frequency domain features and they are combined with spatial features so that the features that are not revealed in spatial domain will be extracted and the classification performance can be tuned up. The proposed CADx comprises of four stages. In the first stage, lung region is segmented using Convexity based active contour segmentation. At second stage ROIs are extracted using spatially constrained KFCM clustering. Followed by standard wavelet transforms is applied on ROI so that transform domain features are extracted with shape and haralick histogram features. Finally neural network is trained by combined feature set to identify the cancerous nodules. Our proposed scheme has shown sensitivity of 95% and specificity of 96%.

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Section: Fig 4 : Wavelet Decompositionmentioning

confidence: 99%

Computer Aided Analysis of Lung Ct Based on Transform Domain Features

Minnal

2014

IJCSI

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Vehicle Model Recognition Based On Using Image Processing And Wavelet Analysis

Abadi

Amiri

Goharimanesh

et al. 2015

International Journal on Smart Sensing and Intelligent Systems

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Bone Mineral Density Screening System Using CMOS-Sensor X-ray Detector

Maneerat

Visitsattapongse

Pintavirooj

2021

Sensors

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This research concerns a design and construction of a bone mineral density (BMD) and bone mineral content (BMC) measurement system based on dual energy X-ray absorptiometry (DEXA). An indirect X-ray detector is designed by optical coupling CMOS sensor with image on the intensifying screen. A dedicated microcontroller X-ray apparatus is used as an X-ray source to capture two energy level X-ray of middle phalanges bone of middle finger. The captured image is processed based on modified Beer-Lambert law to compute bone mineral density. Bone mineral content is also computed by determining the area of the phalanges bone using active contour. The designed bone mineral density (BMD) and bone mineral content (BMC) measurement system is low-cost and hence can be distributed at district hospital for screening purposes of Osteoporosis of the elderly. Compared with BMD measured from commercial model, BMD measurement of our system acquires linear relation with R2 equals 0.969. The mean square error between the normalized BMD value and that of the commercial model is 0.0000981.

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Characterization of bone Tissue by microwaves using wavelets and KNN

Cited by 3 publications

References 2 publications

Computer Aided Analysis of Lung Ct Based on Transform Domain Features

Computer Aided Analysis of Lung Ct Based on Transform Domain Features

Vehicle Model Recognition Based On Using Image Processing And Wavelet Analysis

Bone Mineral Density Screening System Using CMOS-Sensor X-ray Detector

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