scholarly journals Polycystic Ovarian Syndrome Detection by Using Two-Stage Image Denoising

2021 ◽  
Vol 38 (4) ◽  
pp. 1217-1227
Author(s):  
Shruti Bhargava Choubey ◽  
Abhishek Choubey ◽  
Durgesh Nandan ◽  
Anurag Mahajan
Keyword(s):  
Polycystic Ovarian Syndrome ◽  
Median Filter ◽  
Wiener Filter ◽  
Speckle Noise ◽  
Invasive Technique ◽  
Noise Pattern ◽  
Area Unit ◽  
State Of Affairs ◽  
Pattern Information ◽  
Ovarian Syndrome

The requirement of imaging methods in the medical field is vivid. If the capturing devices are not sophisticated, the acquired images will have a significant amount of noise. These noises are hazardous and cannot be entertained. Polycystic Ovarian Syndrome (PCOS) caused the state of affairs in girls if not diagnosed and look after early stages. Tran's epithelial duct ultrasound machine could be a non-invasive technique of imaging the human ovary to show salient options necessary for PCOS identification. Numbers of follicles and their sizes area unit the most options that characterize ovarian pictures. Hence, PCOS is diagnosed by investigating the numbers of follicles and measurement their sizes manually. conflict in medical aid is essentially created by technical advances in modalities that resulted from fruitful interactions among the essential science, bioscience, and manufacturer. Hence, PCOS is diagnosed by investigating the numbers of follicles and measurement their sizes manually. This paper attempts to identify the noise & try to generate a noise-free image by evaluation of noise properties. The noise pattern information thus provides an upper hand in the second stage filtering with specific filters i.e. fuzzified. A median filter for salt and pepper noise; and an adaptive wiener filter for Gaussian noise. 46.2%, 15.1%, and 12.4% improvement in MSE for salt & pepper, Gaussian, and speckle noise as compared to best existing methods.

scholarly journals Review on automated follicle identification for polycystic ovarian syndrome

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
A A Nazarudin ◽  
Noraishikin Zulkarnain ◽  
A. Hussain ◽  
S. S. Mokri ◽  
I. N. A. M. Nordin
Keyword(s):  
Performance Evaluation ◽  
Ultrasound Imaging ◽  
Polycystic Ovarian Syndrome ◽  
Speckle Noise ◽  
Future Research ◽  
Health Treatment ◽  
Time Consumption ◽  
Non Invasive ◽  
And Performance ◽  
Ovarian Syndrome

Polycystic Ovarian Syndrome (PCOS), is a condition of the ovary consisting numerous follicles. Accurate size and number of follicles detected are crucial for treatment. Hence the diagnosis of this condition is by measuring and calculating the size and number of follicles existed in the ovary. For diagnosis, ultrasound imaging has become an effective tool as it is non-invasive, inexpensive and portable. However, the presence of speckle noise in ultrasound imaging has caused an obstruction for manual diagnosis which are high time consumption and often produce errors. Thus, image segmentation for ultrasound imaging is critical to identify follicles for PCOS diagnosis and proper health treatment. This paper presents different methods proposed and applied in automated follicle identification for PCOS diagnosis by previous researchers. In this paper, the methods and performance evaluation are identified and compared. Finally, this paper also provided suggestions in developing methods for future research.

scholarly journals Comparative Image Quality Analysis of Spatial Filters for Pre-processing of CT Abdominal Images

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 1449-1469
Author(s):  
Ramya Mohan ◽  
S.P. Chokkalingam ◽  
Kirupa Ganapathy ◽  
A. Rama
Keyword(s):  
Image Processing ◽  
Gaussian Noise ◽  
Median Filter ◽  
Wiener Filter ◽  
Speckle Noise ◽  
Ct Images ◽  
Spatial Filters ◽  
Abdominal Ct ◽  
Wiener Filters ◽  
Salt And Pepper

Aim: To determine the efficient noise reduction filter for abdominal CT images. Background: Image enrichment is the first and foremost step that has to be done in all image processing applications. It is used to enhance the quality of digital images. Digital images are liable to addition of noise from various sources such as error in instrument calibration, excess staining of images, etc., Image de-noising is an enhancement technique used to remove / reduce noise present in an image. Reducing the noise of images and preserving its edges are always critical and challenging in image processing. Materials and Method: In this paper, four different spatial filters namely Mean, Median, Gaussian and Wiener were used on 100 CT abdominal images and their performances were compared against the following four parameters: Peak signal to noise ratio (PSNR), Mean Square Error (MSE), Normalised correlation coefficient (NCC) and Normalised Absolute Error (NAE) to determine the best denoising filter for the abdominal CT images. Result: Based on the experimental parameters, the median filter had the maximum efficiency in managing salt and pepper noise than the other three filters. Both Median and Wiener filters showed efficiency in removing the Gaussian noise. Whereas, the Wiener filter demonstrated higher efficiency in reducing both Poisson and Speckle noise. Conclusion: Based on the results of this study, we can conclude that the median filter can be used to reduce Salt and Pepper noises. Median and Wiener filters are significantly better for Gaussian Noise and the Wiener filter can be used to reduce both Poisson & Speckle noise in abdominal CT images.

scholarly journals Removal of various Noises in Dental X-ray Images using Selective Median Filter

2019 ◽  
Vol 8 (12) ◽  
pp. 2550-2558
Keyword(s):  
Image Processing ◽  
Gaussian Noise ◽  
Median Filter ◽  
Medical Image Analysis ◽  
Wiener Filter ◽  
Speckle Noise ◽  
Linear Filters ◽  
Severe Problem ◽  
X Ray ◽  
Non Linear

Image processing plays major role to provide additional information in medical diagnosis. Input images contain picture information as well as noise information. Noise information is added with the images during signal acquisition stage or in the transmission of image data. Salt & Pepper noise, Gaussian noise and Speckle noise is the major noises introduced in the images. Noise information may be interpreted as data and it may lead to severe problem. Linear and Non-linear filters are used to reduce these noises in the images. In medical image analysis, non-linear filters are preferred over linear filters because it preserves edge information. Dental X-ray image is used to identify the cavities and its depth. Average filter, median filter and wiener filter are the classical techniques used in many image processing applications. In this paper, three different noises (Salt &pepper, Gaussian and Speckle noise) are added and different filters (Average filters, median filter, Wiener filter) performances are analysed with the PSNR, SNR and MSE. Analysis shows that median filter is suitable for reducing salt & pepper noise and wiener filter is suitable for reducing Gaussian noise and speckle noise in the dental x-ray images. Selective median filter is a modified wiener filter. Median filter is used for the pixel value 0 and 255.For other pixel values wiener filter is used. Selective median filter is giving better result than traditional techniques

scholarly journals Performance Evaluation of Image Enhancement Techniques in Spatial and Wavelet Domains

2012 ◽  
Vol 3 (1) ◽  
pp. 162-166
Author(s):  
Amardeep Singh Virk ◽  
Mandeep Kaur ◽  
Lovely Passrija
Keyword(s):  
Signal To Noise Ratio ◽  
Wavelet Coefficient ◽  
Median Filter ◽  
Wiener Filter ◽  
Speckle Noise ◽  
Spatial Filter ◽  
Ultrasound Images ◽  
Filter Methods ◽  
Significant Research ◽  
Wavelet Methods

Denoising is one of the important tasks in image processing. Despite the significant research conducted on this topic, the development of efficient denoising methods is still a compelling challenge. In this paper, spatial domain methods and Wavelet Domain Methods of image denoising have been evaluated. The medical ultrasound images suffer from speckle noise which is multiplicative in nature and more difficult to remove than additive noise. In the spatial filter methods Median Filter and Wiener Filter are implemented. These methods are based on the simple formulas that are proposed by different authors. In Wavelet Methods Visu Shrink, Neigh shrink and Bayes Shrink are implemented. The basic idea of wavelet methods is to denoise the image by applying wavelet transform to the noisy image, then thresholding the detailed wavelet coefficient and inverse transforming the set of thresholded coefficient to obtain the denoised image. The comparison of all filters methods is done using various Quality Metrics like Peak Signal-to-Noise Ratio (PSNR), Bit Error Rate (BER), Mean Square Error, etc. The filters methods implemented in MATLAB 7.10.0.499(R2010a).

Characteristics of Polycystic Ovarian Syndrome in Sub-fertile Woman Attending to GOPD in ShSMCH

2019 ◽  
Vol 11 (1) ◽  
pp. 17-23
Author(s):  
Jinnat Ara Islam ◽  
Fatema Ashraf ◽  
Eva Rani Nandi
Keyword(s):  
Polycystic Ovarian Syndrome ◽  
Polycystic Ovaries ◽  
Cross Sectional ◽  
Medical College ◽  
Appropriate Treatment ◽  
Pcos Group ◽  
Symptom Complex ◽  
Ovarian Syndrome ◽  
Selection Of ◽  
Fertile Women

Background: Polycystic ovarian syndrome (PCOS) is a condition characterized by menstrual abnormalities (oligo/amenorrhea) and clinical or biochemical features of hyperandrogenism and may manifest at any age. It is a common cause of female subfertility. All the dimensions of PCOS have not been yet completely explored. Methods: It was a cross sectional comparative study carried out at-GOPD of Shaheed Suhrawardy Medical College & Hospital from January, 2016 to December 2016 on 162 subfertile women. Among them 54 were PCOS group and 108 were non PCOS group. PCOS was diagnosed by (Rotterdam criteria 2003) (i) Oligo or anovulation (ii) hyperandrogenism (iii) Polycystic ovaries. Study was done to evaluate and compare the demographic characteristics, clinical, biochemical and ultrasoundgraphic features of sub-fertile women with and without PCOS. Results: A total of 162 sub-fertile women aged 16-36 years. Mean age was 29.5±5.4. There were significant differences between the two groups in terms of (oligo/amenorrhea), hirsutism, WHR and ovarian ultrasound features. There were no significant differences between two groups in correlations between the level of obesity with the incidence of anovulation, hyperandrogenism or with hormonal features. Conclusion: PCOS is one of the important factors causing Infertility. It is an ill-defined symptom complex needed due attention. There is a need to increase awareness regarding. The clinical features of PCOS are heterogenous thus can be investigated accordingly of selection of appropriate treatment modality. J Shaheed Suhrawardy Med Coll, June 2019, Vol.11(1); 17-23

Impaired DHEA sulfation defines androgen excess in women with polycystic ovarian syndrome (PCOS)

2016 ◽  
Author(s):  
Punith Kempegowda ◽  
Michael W O'Reilly ◽  
Zaki Hassan-Smith ◽  
Karl-Heinz Storbeck ◽  
Angela E Taylor ◽  
...  
Keyword(s):  
Polycystic Ovarian Syndrome ◽  
Androgen Excess ◽  
Ovarian Syndrome

Visceral fat drives 5[alpha]-reductase activity independent of BMI in women with polycystic ovarian syndrome

2015 ◽  
Author(s):  
Punith Kempegowda ◽  
Michael W O'Reilly ◽  
Nicola J Crabtree ◽  
Angela E Taylor ◽  
Beverly A Hughes ◽  
...  
Keyword(s):  
Visceral Fat ◽  
Polycystic Ovarian Syndrome ◽  
Reductase Activity ◽  
Ovarian Syndrome
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