A classification of polycystic Ovary Syndrome based on follicle detection of ultrasound images

Purnama, Bedy; Wisesti, Untari Novia; Adiwijaya, Adiwijaya; Nhita, Fhira; Gayatri, Andini; Muti’ah, Titik

doi:10.1109/icoict.2015.7231458

Cited by 48 publications

(12 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A typical object detection algorithm [4] used in defective product detection or classification is as in Figure 1. In this study, follicles in the ovaries were determined by using some of these procedures shown in Figure 1.These are pre-processing and segmentation.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Follicle Detection for Polycystic Ovary Syndrome by using Image Processing Methods

Yilmaz

Özmen

2020

International Journal of Applied Mathematics Electronics and Computers

View full text Add to dashboard Cite

Polycystic ovary syndrome is a hormonal disorder seen in many women. It occurs by the combination of many small and benign cysts in the ovaries. These cysts, called follicles, create a special pattern in the ovaries observed with ultrasound imaging. The number, structure, and size of these follicles provide important information for the diagnosis of ovarian diseases. In this study, two different methods of follicle detection are tested for Polycystic Ovary Syndrome. The first method consists of noise filtering, contrast adjustment, binarization, and morphological processes. For this method, Median Filter, Average Filter, Gaussian Filter, and Wiener Filter were used for noise reduction, and then histogram equalization and adaptive thresholding were tested. For the second method, Gaussian Filter and Wavelet Transform were selected for noise reduction, and k-means clustering and morphological operations were applied to the images. In the segmentation phase performed for both methods, follicles were detected with the Canny Edge Detection algorithm. False Acceptance Rate (FAR) and False Rejection Rate (FRR) were used to evaluate the accuracy of the results. Our results show that the most accurate follicle detection was obtained by using the Wiener Filter and Gaussian Filter.

show abstract

Section: Methodsmentioning

confidence: 99%

“…There are some studies on follicle detection in the literature. Purnama et al [4] designed an application to classify patients with polycystic ovary syndrome and performed feature extraction with Gabor wavelets. Three different classification algorithms were compared using normal and polycystic ovarian images.…”

Section: Introductionmentioning

confidence: 99%

Follicle Detection for Polycystic Ovary Syndrome by using Image Processing Methods

Yilmaz

Özmen

2020

International Journal of Applied Mathematics Electronics and Computers

View full text Add to dashboard Cite

show abstract

“…Test dataset performance was similar to the performance of the training dataset. "A Classification of Polycystic Ovary Syndrome Based on Follicle Detection of Ultrasound Images" [14], which proposed 80 images, consisting of 60 normal ovary images and 20 images of PCOS ovary. Ovaries are of 2 types: normal and polycystic (multiple small cysts).…”

Section: Fig 1 Polycystic and Normal Ovary[4]mentioning

confidence: 99%

Genetic Clustering for Polycystic Ovary Syndrome Detection in Women of Reproductive Age

Thakare,

Lele

2020

IJEAT

View full text Add to dashboard Cite

Now a days, hormonal disorder causing Polycystic Ovary Syndrome (PCOS) is been observed in most of the women of reproductive age. PCOS causes enlarged ovaries with small cysts on the outer edges. Women with PCOS may have irregularity in menstrual periods or excess male hormone (androgen) levels. The ovaries may develop numerous small collections of follicles (cysts) and fail to regularly release eggs. Symptoms of PCOS include irregular periods, excess androgen, polycystic ovaries, abnormal Body Mass Index, disturbed levels of hormones (Luteinizing Hormone, Follicle-stimulating Hormone, Dehydroepiandros-terone), poor insulin resistance. There is a need to design and develop an optimized system to analyze the sonogram in correlation with the physical symptoms for detection of PCOS at early stage which may result in proper treatment and reduced health loss. This article presents work-in-progress of our proposed research on Intelligent System to detect PCOS. The performance analysis of various Machine learning algorithms like Artificial Neural Network, K-nearest Neighbor and Linear Regression to detect PCOS is presented. Whereas, optimized Genetic Clustering for optimization of classification results is proposed. Basic Genetic Algorithm (GA)and other hybrid GA's will be used for comparing the optimal results. The classification results are optimized with 89% accuracy

show abstract

“…Then, the input features were mapped in the range of -1 to +1 prior to inputs of the classifier. In this study, RBF kernel SVM was chosen due to its good performance in the pattern recognition area (Cheng & xu 2007;Kusy 2004;Sahay et al 2016;Hall 2015;Yan & Wang 2009;Banerjee et al 2015;Purnama et al 2015;Meddeb & Karray 2015). In the selection of the optimal model of SVM classifier with RBF kernel, both C and σ varies from 10 to 100 with increment of 10.…”

Section: Extraction Of Skeleton Joint Pointsmentioning

confidence: 99%

Kinect-Based Human Gait Recognition Using Locally Linear Embedded and Support Vector Machine

Tahir¹,

Sahak²,

Yassin³

et al. 2018

JKUKM

View full text Add to dashboard Cite

Recognition of human gait could be performed effectively provided that significant gait features are well extracted along with effective recognition process. Thus, the gait features should be selected or optimized appropriately for optimal accuracy during recognition. Therefore, in this research, optimization of gait features for both oblique and frontal view are evaluated for recognition purpose using Locally Linear Embedded (LLE) along with multi-class Support Vector Machine (SVM). Firstly, dynamic gait features for one gait cycle are extracted from each subject's walking gait that is acquired using Kinect sensor. Next, the extracted gait features were then optimized using LLE known as DG-LLE and further classified by multi-class SVM with Error Correcting Output Code (ECOC) algorithm. Further, to validate the effectiveness of LLE as optimization technique, the proposed method is then compared with another two gait features namely the original gait features known as DG and optimization using Principal Component Analysis labeled as DG-PCA. Results showed that the optimization based on DG-LLE outperformed the other two methods namely DG and DG-PCA for both oblique and frontal views. In addition, DG-LLE method contributed as the highest recognition rate for both frontal and oblique views. Results also confirmed that the accuracy rate for frontal view is higher specifically 98.33% as compared to oblique view with 94.67%.

show abstract

A classification of polycystic Ovary Syndrome based on follicle detection of ultrasound images

Cited by 48 publications

References 6 publications

Follicle Detection for Polycystic Ovary Syndrome by using Image Processing Methods

Follicle Detection for Polycystic Ovary Syndrome by using Image Processing Methods

Genetic Clustering for Polycystic Ovary Syndrome Detection in Women of Reproductive Age

Kinect-Based Human Gait Recognition Using Locally Linear Embedded and Support Vector Machine

Contact Info

Product

Resources

About