Follicle Detection for Polycystic Ovary Syndrome by using Image Processing Methods

Yilmaz, Perihan Gülşah; Özmen, Güzin

doi:10.18100/ijamec.803400

Cited by 9 publications

(4 citation statements)

References 11 publications

(10 reference statements)

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“…In this way, unimportant details were removed, and important regions were highlighted. In this study, two different methods were used for the pre-processing stage [25].…”

Section: Follicle Detectionmentioning

confidence: 99%

Otomatik Folikül Saptama Yöntemleri Kullanılarak ESA Tabanlı Polikistik Over Sendromu Tespiti

YILMAZ,

ÖZMEN,

ALPTEKİN

2023

Journal of Polytechnic

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The aim of this study was to determine the best method for follicle detection using ovarian ultrasound images and to classify the ultrasound images as pcos or normal ovaries using the proposed CNN architecture. Two different methods for follicle detection have been proposed to evaluate pcos. For this purpose, the Median, the Mean, the Wiener, and the Gaussian filters were tested using standard and adaptive thresholds. Second, Gaussian filtering, Discrete Wavelet Transform, and k-means clustering algorithms were tested. The Canny operator separates follicles from the background in the segmentation phase. In this study, a CNN architecture that classifies limited ultrasound ovary images was developed, and its success in the best follicle detection method was presented. The highest follicle detection accuracy of 97.63% was achieved with adaptive thresholding using a Wiener filter. Besides, the ultrasound images of the ovaries were classified as "normal" or "polycystic ovary syndrome" using CNN architecture with classification accuracy of 65.81% for unsegmented ovarian images and 77.81% for segmented images. In addition to the proposed method, classification was performed using SqueezeNet-based transfer learning, which was successful in limited datasets, and 74.18% classification accuracy was achieved for the unsegmented images and 75.54 % for segmented images . The results show that the combination of the Wiener filter with adaptive thresholding was quite successful in follicle detection and that the CNN can better classify ovaries using preprocessed ultrasound images.

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“…In this way, unimportant details were removed, and important regions were highlighted. In this study, two different methods were used for the pre-processing stage [25].…”

Section: Follicle Detectionmentioning

confidence: 99%

Otomatik Folikül Saptama Yöntemleri Kullanılarak ESA Tabanlı Polikistik Over Sendromu Tespiti

YILMAZ,

ÖZMEN,

ALPTEKİN

2023

Journal of Polytechnic

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“…For example, Mandal et al 15 suggested a technique to diagnose PCOS by automatically segmenting cysts and follicle regions from ultrasound images; for which they employed multiple digital image processing steps such as histogram equalization, K-means clustering, median filtering, and morphological erosion on 19 ultrasound pictures. Yilmaz et al 16 tested and compared two methods of follicle detection using image processing techniques to diagnose PCOS; where the first approach includes noise reduction (Median, Average, Gaussian, and Wiener Filters), contrast modification (histogram equalization and adaptive thresholding), binarization, and morphological procedures. ; and the second approach comprises of noise reduction (Gaussian Filter and Wavelet Transform), k-means clustering, hole filling and morphological operations.…”

Section: Related Workmentioning

confidence: 99%

An extended machine learning technique for polycystic ovary syndrome detection using ovary ultrasound image

Suha

Islam

2022

Sci Rep

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Polycystic ovary syndrome (PCOS) is the most prevalent endocrinological abnormality and one of the primary causes of anovulatory infertility in women globally. The detection of multiple cysts using ovary ultrasonograpgy (USG) scans is one of the most reliable approach for making an accurate diagnosis of PCOS and creating an appropriate treatment plan to heal the patients with this syndrome. Instead of depending on error-prone manual identification, an intelligent computer-aided cyst detection system can be a viable approach. Therefore, in this research, an extended machine learning classification technique for PCOS prediction has been proposed, trained and tested over 594 ovary USG images; where the Convolutional Neural Network (CNN) incorporating different state-of-the-art techniques and transfer learning has been employed for feature extraction from the images; and then stacking ensemble machine learning technique using conventional models as base learners and bagging or boosting ensemble model as meta-learner have been used on that reduced feature set to classify between PCOS and non-PCOS ovaries. The proposed technique significantly enhances the accuracy while also reducing training execution time comparing with the other existing ML based techniques. Again, following the proposed extended technique, the best performing results are obtained by incorporating the “VGGNet16” pre-trained model with CNN architecture as feature extractor and then stacking ensemble model with the meta-learner being “XGBoost” model as image classifier with an accuracy of 99.89% for classification.

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“…In ultrasound device, two-dimensional ovary images in grey and white/black colour are generated. The generated polycystic ovarian and normal ultrasound images have been different from one another [13,14]. Normally, the patients affected by PCOS consist of 10-12 cysts present in the ovary, but more than 10 cysts are more enough to diagnose the disorder from the ultrasound images.…”

Section: Srinivas Publicationmentioning

confidence: 99%

Medical Image Processing: Detection and Prediction of PCOS – A Systematic Literature Review

Pulluparambil

Bhat

2021

IJHSP

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Purpose: Considered as the most common hormonal disorder among women, polycystic ovary syndrome or PCOS affects 1 in 10 reproductive aged women (18 - 44 years). Ultrasonography is applied for assessing the ovaries to detect PCOS. The patients affected by PCOS consist of 10-12 cysts present in the ovary, but more than 10 cysts are more enough to diagnose the disorder from the ultrasound images. Then, by examining the ultrasound the presence of follicles will be determined. Therefore, the image processing approaches have assisted to identify the characteristics like follicle size, number of follicles and structure to minimize the workload and time of doctors. PCOS do not have better treatment and effective diagnosis. This paper includes reviewing a summary of some of the researches that have been going in area of medical diagnosis. Based on the review, research gap, research agendas to carry out further research are identified. Approach: A detailed study on the algorithms used in medical image processing and classification. Findings: The study indicated that most of the classification of polycystic ovarian syndrome is done merely on the clinical data sets. The new hybrid methodology proposed will be more precise as both images and lifestyle are analysed. Originality: The type of data required for detection system are studied and the architecture and schematic diagram of a proposed system are included. Paper Type: Literature Review.

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Follicle Detection for Polycystic Ovary Syndrome by using Image Processing Methods

Cited by 9 publications

References 11 publications

Otomatik Folikül Saptama Yöntemleri Kullanılarak ESA Tabanlı Polikistik Over Sendromu Tespiti

Otomatik Folikül Saptama Yöntemleri Kullanılarak ESA Tabanlı Polikistik Over Sendromu Tespiti

An extended machine learning technique for polycystic ovary syndrome detection using ovary ultrasound image

Medical Image Processing: Detection and Prediction of PCOS – A Systematic Literature Review

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