Digital image processing approach using combined wavelet hidden Markov model for well-being analysis of insulators

2011 ◽  
Vol 5 (2) ◽  
pp. 171 ◽  
Author(s):  
V.S. Murthy ◽  
S. Gupta ◽  
D.K. Mohanta
Keyword(s):  
Image Processing ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Digital Image Processing ◽  
Digital Image ◽  
Hidden Markov ◽  
Well Being ◽  
Processing Approach

scholarly journals Pengenalan Gangguan Ginjal Melalui Iridologi Menggunakan Hidden Markov Model (HMM)

2019 ◽  
Vol 9 (2) ◽  
pp. 19-26
Author(s):  
Reza Satria Rinaldi ◽  
Wagiasih Wagiasih ◽  
Ika Novia Anggraini
Keyword(s):  
Image Processing ◽  
Singular Value Decomposition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Singular Value ◽  
Parameter Determination ◽  
Iris Image ◽  
Determination System ◽  
Value Decomposition

ABSTRACTIridology has not been widely applied for the recognition of kidney disorders. identification of kidney disorders through iris image using iridology chart, can make it easier to make diagnosis to find out about kidney disorders. The method used in the process of recognition of kidney disorders through iridology is the Hidden Markov Model (HMM) method, with a HMM parameter determination system using the calculation of the koefisien Singular Value Decomposition (SVD) coefficient. The size of the codebook used is 7, i.e. 16, 32, 64, 128, 256, 512 and 1024. Different sizes of codebooks will result in different recognition times. The time needed will be longer when the size of the codebook is getting bigger. The accuracy of the process of recognition of kidney disorders through iridology using the HMM method in this study is 68.75% for codebook 16, 87.5% for codebook 32, 100% for codebook 128 and 100% for codebook 512. Keywords : iridology, codebook, image processing, singular value decomposition (SVD), Hidden Markov Model (HMM).

scholarly journals Mobile Screening Framework of Anterior Segment Photographed Images

2018 ◽  
Vol 7 (4.11) ◽  
pp. 85
Author(s):  
N Syahira M Zamani ◽  
Laily Azyan Ramlan ◽  
W Mimi Diyana W Zaki ◽  
Aini Hussain ◽  
Haliza Abdul Mutalib
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Anterior Segment ◽  
Qualitative Assessment ◽  
Computational Time ◽  
Future Studies ◽  
Processing Approach ◽  
Processing Platform ◽  
Mobile Screening

This work presents a qualitative measurement of anterior segment photographed images (ASPIs) to identify between normal eyes and eyes with pterygium and pinguecula through Otsu multi-thresholding approach without contrast enhancement. In addition, we also propose a mobile screening framework of ASPIs through smartphones. ASPIs were directly sent to the cloud storage once an ASPI was captured using a smartphone camera, and then each image was processed through a digital image processing approach in a processing platform. Three important steps, namely, pre-processing, image segmentation and qualitative assessment, are involved in the processing platform of the mobile screening framework. The ASPIs are pre-processed to minimise or eliminate any unwanted areas within the image. Then, these ASPIs are segmented through multi-thresholding Otsu approach with clustering number n = 3. Segmentation result shows that the accuracy of the proposed method is 87.5%, which is comparable with the previously established work that has applied three-step differencing (3SD) method. However, the proposed approach has better computational time which is six times faster than the 3SD method. These results demonstrate a remarkable effort to produce a simple but straightforward digital image processing approach to be implemented in cloud computing for future studies.                                                                                       

Sign in / Sign up

Export Citation Format

Share Document