scholarly journals A Deep Convolutional Architectural Framework for Radiograph Image Processing at Bit Plane Level for Gender & Age Assessment

2020 ◽  
Vol 62 (2) ◽  
pp. 679-694
Author(s):  
N. Shobha Rani ◽  
Chandrajith M ◽  
Pushpa B. R ◽  
Bipin Nair B. J
Keyword(s):  
Image Processing ◽  
Age Assessment ◽  
Architectural Framework ◽  
Bit Plane ◽  
Radiograph Image

scholarly journals Versatile Framework for Medical Image Processing and Analysis with Application to Automatic Bone Age Assessment

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Chen Zhao ◽  
Jungang Han ◽  
Yang Jia ◽  
Lianghui Fan ◽  
Fan Gou
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Medical Image ◽  
Deep Neural Networks ◽  
Medical Image Processing ◽  
Bone Age ◽  
Age Assessment ◽  
Image Processing And Analysis ◽  
Learning Technique ◽  
Bone Age Assessment

Deep learning technique has made a tremendous impact on medical image processing and analysis. Typically, the procedure of medical image processing and analysis via deep learning technique includes image segmentation, image enhancement, and classification or regression. A challenge for supervised deep learning frequently mentioned is the lack of annotated training data. In this paper, we aim to address the problems of training transferred deep neural networks with limited amount of annotated data. We proposed a versatile framework for medical image processing and analysis via deep active learning technique. The framework includes (1) applying deep active learning approach to segment specific regions of interest (RoIs) from raw medical image by using annotated data as few as possible; (2) generative adversarial Network is employed to enhance contrast, sharpness, and brightness of segmented RoIs; (3) Paced Transfer Learning (PTL) strategy which means fine-tuning layers in deep neural networks from top to bottom step by step to perform medical image classification or regression tasks. In addition, in order to understand the necessity of deep-learning-based medical image processing tasks and provide clues for clinical usage, class active map (CAM) is employed in our framework to visualize the feature maps. To illustrate the effectiveness of the proposed framework, we apply our framework to the bone age assessment (BAA) task using RSNA dataset and achieve the state-of-the-art performance. Experimental results indicate that the proposed framework can be effectively applied to medical image analysis task.

scholarly journals Computer-Assisted Bone Age Assessment: Graphical User Interface for Image Processing and Comparison

2004 ◽  
Vol 17 (3) ◽  
pp. 175-188 ◽  
Author(s):  
Ewa Pietka ◽  
Arkadiusz Gertych ◽  
Sylwia Pospiech–Kurkowska ◽  
Fei Cao ◽  
H.K. Huang ◽  
...  
Keyword(s):  
Image Processing ◽  
User Interface ◽  
Graphical User Interface ◽  
Bone Age ◽  
Computer Assisted ◽  
Age Assessment ◽  
Bone Age Assessment

scholarly journals Image processing of periapical radiograph on granuloma detection by analysis method based on Android

2021 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Merry Annisa Damayanti ◽  
Suhardjo Sitam ◽  
Bambang Hidayat ◽  
Ivhatry Rizky Octavia Putri Susilo
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Discrete Wavelet ◽  
Android Application ◽  
Analysis Method ◽  
Large Object ◽  
Cross Sectional ◽  
Questionnaire Assessment ◽  
Radiograph Image

Objectives: The study assesses periapical radiograph image with various android based analysis method to detect granuloma. Materials and Methods: The study uses survey descriptive cross sectional by using questionnaire. The questionnaire is distributed to 70 random respondents. The methods of the android application used are BLOB (Binary Large Object), DCT and LDA (Discrete Cosine Transform and Linier Discriminant Analysis), DWT and PCA (Discrete Wavelet Transform & Principal Component Analysis), and multiwavelet transformation. The questionnaire assessment included accuracy, effectiveness, attractiveness, innovativeness of the android application. Results: Android application with BLOB has effectivity and accuracy of 62,5%, attractiveness and innovativeness of 75%. Android application with DCT and LDA has effectivity and accuracy of 50 %, attractiveness of 70% and innovativeness of 80%. Android application with DWT and PCA has effectivity of 50%, accuracy of 60%, attractiveness of 66,66% and innovativeness of 80%. Android application with multiwavelet transformation has effectivity and accuracy of 50%, attractiveness of 55% and innovativeness of 73%. Conclusion: Based on assessment, the four methods used to detect granuloma are effective and applicative with android-based application. Android-based Application can detect granuloma with approximately more than 70% successful rate. These methods ease the practitioner to interpret the granuloma image.

A Digital Watermarking Algorithm Based on the Wavelet Bit Plane Coding

2013 ◽  
Vol 821-822 ◽  
pp. 1438-1441
Author(s):  
Gao Yan ◽  
Yan Liang ◽  
Xin Zhou ◽  
Chun Xia Qi
Keyword(s):  
Image Processing ◽  
Digital Watermarking ◽  
Original Image ◽  
A Algorithm ◽  
Bit Plane Coding ◽  
Bit Plane ◽  
The Difference ◽  
Bit Planes ◽  
Processing Techniques ◽  
Signal Processing Techniques

In this paper a algorithm of digital image watermark based on wavelet bit plane is introduced, and the original image is not required for detecting the watermarking. The digital watermark is embedded by changing information of some bit planes in DWT images at different resolutions. The watermark can be extracted on the difference bit plane values of subimages of the decomposed watermarked image which is then mapped to an image with a few shades of gray. Experimental results show that the watermark is robust to several signal processing techniques, including JPEG compression and some image processing operations

Some comments about observations and image processing of comet 29P/Schwassmann-Wachmann 1

1999 ◽  
Vol 173 ◽  
pp. 243-248
Author(s):  
D. Kubáček ◽  
A. Galád ◽  
A. Pravda
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Harvard College ◽  
Oak Ridge ◽  
Large Scale Structures ◽  
Short Period Comet ◽  
Short Period ◽  
Scale Structures ◽  
Harvard College Observatory ◽  
Period Comet

AbstractUnusual short-period comet 29P/Schwassmann-Wachmann 1 inspired many observers to explain its unpredictable outbursts. In this paper large scale structures and features from the inner part of the coma in time periods around outbursts are studied. CCD images were taken at Whipple Observatory, Mt. Hopkins, in 1989 and at Astronomical Observatory, Modra, from 1995 to 1998. Photographic plates of the comet were taken at Harvard College Observatory, Oak Ridge, from 1974 to 1982. The latter were digitized at first to apply the same techniques of image processing for optimizing the visibility of features in the coma during outbursts. Outbursts and coma structures show various shapes.

Analysis of the 9th November 1990 flare

2000 ◽  
Vol 179 ◽  
pp. 229-232
Author(s):  
Anita Joshi ◽  
Wahab Uddin
Keyword(s):  
Image Processing ◽  
Two Dimensional ◽  
Temporal Behaviour ◽  
Contour Maps ◽  
Dimensional Measurements ◽  
Processing Software ◽  
Image Processing Software

AbstractIn this paper we present complete two-dimensional measurements of the observed brightness of the 9th November 1990Hαflare, using a PDS microdensitometer scanner and image processing software MIDAS. The resulting isophotal contour maps, were used to describe morphological-cum-temporal behaviour of the flare and also the kernels of the flare. Correlation of theHαflare with SXR and MW radiations were also studied.

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