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The presented study aims to design a computer-aided detection and diagnosis system for breast dynamic contrast enhanced magnetic resonance imaging. In the proposed system, the segmentation task is performed in two stages. The first stage is called breast region segmentation in which adaptive noise filtering, local adaptive thresholding, connected component analysis, integral of horizontal projection, and breast region of interest detection algorithms are applied to the breast images consecutively. The second stage of segmentation is breast lesion detection that consists of 32-class Otsu thresholding and Markov random field techniques. Histogram, gray level co-occurrence matrix and neighboring gray tone difference matrix based feature extraction, Fisher score based feature selection and, tenfold and leave-one-out cross-validation steps are carried out after segmentation to increase the reliability of the designed system while decreasing the computational time. Finally, support vector machines, k-nearest neighbor, and artificial neural network classifiers are performed to separate the breast lesions as benign and malignant. The average accuracy, sensitivity, specificity, and positive predictive values of each classifier are calculated and the best results are compared with the existing similar studies. According to the achieved results, the proposed decision support system for breast lesion segmentation distinguishes the breast lesions with 86%, 100%, 67%, and 85% accuracy, sensitivity, specificity, and positive predictive values, respectively. These results show that the proposed system can be used to support the radiologists during a breast cancer diagnosis.

In this paper, our goal is to determine the boundaries of lesion and then calculate the area of existing lesion in breast magnetic resonance (MR) images to provide a useful information to the radiologists. For this purpose, at first stage region growing (RG) method and active contour model (Snake) is applied to the images to make the boundaries of lesion visible. RG method is one of the simplest approaches for image segmentation and provides accurate results with lower computation time due to its seed point initialization step. Snake method molds a closed contour to the boundary of a region in an image and is also popular in medical image segmentation studies. In the presented study, both of these methods are utilized to determine the lesion boundaries. After determining the boundaries of lesion accurately in the second stage of the study, bit-quad method is applied to the segmented images. Bit quad method is used to compute the area and perimeter of binary lesion images based on matching the logical state of regions of image to binary patterns. Finally, to evaluate the performance of the proposed study, computer simulations are performed. It is demonstrated via computer simulations that the lesion area and parameter values are very close to real values. By means of this study it is aimed to support radiologists during diagnosis and assessment of breast lesions.

ÖZBu çalışmada, meme kanserinin teşhisinde yaygın olarak kullanılan modalitelerden biri olan MRG sisteminden elde edilen görüntüler kullanılarak memede oluşan lezyonların sınırlarının belirlenmesi ve lezyon alanının hesaplanmasına yönelik bir sistem geliştirilmiştir. Geliştirilen sistem, radyologlara büyük kolaylıklar sağlayan ve birçok değiştirilebilir seçenek sunan bir ara yüz üzerinden tasarlanmıştır. Lezyon sınırlarının belirlenmesi ve alanının optimum şekilde hesaplanması için çalışmada dört farklı yöntemden yararlanılmaktadır. Bu yöntemler, eşikleme tabanlı (Otsu eşikleme yöntemi), bulanık mantık tabanlı (bulanık c-ortalama (Fuzzy c-means, FCM)), bölge büyütme tabanlı (Region Growing, RG) ve kümeleme tabanlı (k-ortalama) segmentasyon yöntemlerdir. Otsu, FCM ve RG yöntemleri tek kanallı gri-seviye bölütleme yöntemleridir. K-ortalama yöntemi ise, üç-kanallı renkli görüntüde doğrudan kullanılabilen bir bölütleme yöntemidir. Segmentasyon adımdan sonra, lezyon alanının hesaplanması için bit-dörtlüsü (bitquad) yöntemi uygulanmıştır. Bu aşamalar gerçekleştirildikten sonra geliştirilebilir bir hastane otomasyon sistemi tasarlanmıştır. Tasarlanan sistem uzmana görsel olarak farklı seçenekler sunarak meme lezyonlarını birçok yönden inceleme imkânı sağlamaktadır.Anahtar Kelimeler: Meme kanseri, Manyetik rezonans görüntüleme, segmentasyon, bit dörtlüsü yöntemi. ABSTRACTIn this study, we have developed a system for determining the boundaries of the lesions which come into existence in the breast and calculating the lesion area by using images obtained from the MRI system, which is one of the modalities widely used in diagnosis of the breast cancer. The developed system is designed with an interface that provides great convenience to the radiologists and offers many interchangeable options. In order to determine the boundary of the lesion and to calculate the area optimally, in this study four different methods are utilized. These methods are thresholding based (Otsu thresholding method), fuzzy logic based (fuzzy c-means, FCM), region growing based (Region Growing, RG) and cluster-based (k-means) segmentation methods. The Otsu, FCM and RG methods are single-channel gray-level segmentation methods. In case, the k-means method is a method of segmentation that can be used directly

Abstract-Epilepsy is a neurological disorder resulting from unusual electrochemical discharge of nerve cells in the brain, and EEG (Electroencephalography) signals are commonly used today to diagnose the disorder that occurs in these signals. In this study, it was aimed to use EEG signals to automatically detect pre-epileptic seizure with machine learning techniques. EEG data from two epileptic patients were used in the study. EEG data is passed through the preprocessing stage and then subjected to feature extraction in time and frequency domain. In the feature extraction step 26 features are obtain to determine the seizure time. When the feature vector is analyzed, it is observed that the characteristics of the pre-seizure and non-seizure period are unevenly distributed. A systematic sampling method has been applied for this imbalance. For the balanced data, two test sets with and without Eta correlation are established. Finally, the classification process is performed using the k-Nearest Neighbor classification method. The obtained data are evaluated in terms of Eta-correlated and uncorrelated accuracy, error rate, precision, sensitivity and F-criterion for each channel.