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.
This study presents the classification of cervical disc herniation patients and healthy persons by using muscle fatigue information. Cervical disc herniation patients suffer from neck pain and muscle fatigue in the neck increases these aches. Neck pain is the most common pain type encountered after back pain. The discomforts that occur in the neck region affect the daily quality of life, so the number of researches done in this area is increasing. In this study surface Electromyography (EMG) signals were used to examine muscle fatigue. EMG signals were obtained from Trapezius and Sternocleidomastoid (SCM) muscles in the cervical region of 10 control subject and 10 cervical disc herniation patients. Surface EMG was preferred because it is a noninvasive method. In the first step of this study, EMG signals were filtered and adapted for analysis. In the second step, muscle fatigue was determined using Median and Mean frequency values obtained by Fourier Transform and Welch methods. Feature extraction was the third step which was performed by Short Time Fourier Transform (STFT), Discrete Wavelet Transform (DWT) and Autoregressive method (AR). Finally, Artificial Neural Network (ANN) was used for classification. Training and test data were created by using feature vectors to classify patients with ANN. According to the results, the superior feature extraction method was investigated on patient classification using muscle fatigue information. The best results were obtained by AR method with %99 classification accuracy. Also, the best results were obtained by DWT with %100 classification accuracy for SCM muscle. This study has contributed that AR and DWT are a suitable feature extraction methods for surface EMG signals by providing high accuracy classification with artificial intelligence methods for cervical disc herniation disease. Besides, it is shown that muscle fatigue distinguishes cervical disc herniation patients from healthy people.
ÖzTitrek felç olarak tanımlanan Parkinson hastalığı Alzheimer'dan sonra en sık görülen 2. nörolojik hastalıktır. Son yıllarda, Parkinson hastalığının titreme, depresyon, duruş bozukluğu gibi farklı belirtileriyle ilişkilendirilen çalışmalar yapılmıştır. Bu çalışmada Parkinson hastalarındaki dikkat bozukluğu üzerinde durulmuştur. Dikkat bozukluğu, Parkinson hastalığı olan bireylerde yaygın olarak bulunur ve bireylerin yaşam kalitesi üzerinde önemli bir etkisi vardır. Dikkatin uyarma, yönlendirme ve yürütme etkilerini tek görevde birleştiren Dikkat Ağ Testi, 25 Parkinson hastası ve 21 sağlıklı birey tarafından uygulanmış ve görev tabanlı fonksiyonel görüntüleme tekniği ile kaydedilmiştir. Kaydedilen veriler kafa hareketlerinin düzenlenmesi, işlevsel-yapısal bağdaştırma, segmentasyon, normalleştirme ve bulanıklaştırma ile bir dizi ön işleme aşamasından geçirilmiş ve etki kontrastları oluşturulmuştur. Oluşturulan kontrastlar ile grup analizleri yapılmış ve aktivasyon sonuçları karşılaştırılmıştır. Parkinson hastası ve sağlıklı bireylerin aktivasyonlarına bakıldığında her iki grup için Frontal ve Parietal bölgelerde aktivasyonun yoğunlaştığı, fakat Parkinson hastalarının dikkatin her etkisi için belirgin aktivasyon yoğunluğuna sahip olduğu görülmüştür. Bu çalışmada aynı zamanda Parkinson hastalarındaki dikkat bozukluğunun cinsiyet üzerindeki etkisi de araştırılmıştır. Parkinsona sahip 7'si kadın 18'i erkek toplam 25 bireyin aktivasyon sonuçları karşılaştırılmıştır. Erkek bireylerin kadın bireylere kıyasla uyarma ve yönlendirme etkilerinde, Frontal ve Oksipital bölgelerde belirgin aktivasyon farklarına sahip olduğu görülmüştür. Bu aktivasyon farklarından hareketle Parkinson hastalığına sahip erkek bireylerin görsel bilgiyi işleme ve dikkati yönlendirme becerileri kadın bireylere kıyasla daha başarılı olduğu görülmüştür.
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