Baha Şen

Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques

With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC.

Rapid Automated Classification of Anesthetic Depth Levels using GPU Based Parallelization of Neural Networks

The effect of anesthesia on the patient is referred to as depth of anesthesia. Rapid classification of appropriate depth level of anesthesia is a matter of great importance in surgical operations. Similarly, accelerating classification algorithms is important for the rapid solution of problems in the field of biomedical signal processing. However numerous, time-consuming mathematical operations are required when training and testing stages of the classification algorithms, especially in neural networks. In this study, to accelerate the process, parallel programming and computing platform (Nvidia CUDA) facilitates dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU) was utilized. The system was employed to detect anesthetic depth level on related electroencephalogram (EEG) data set. This dataset is rather complex and large. Moreover, the achieving more anesthetic levels with rapid response is critical in anesthesia. The proposed parallelization method yielded high accurate classification results in a faster time.

Analysis of Demographic Characteristics Creating Coronary Artery Disease Susceptibility Using Random Forests Classifier

Abstract Cardiovascular system diseases are an important health problem. These diseases are very common also responsible for many deaths. With this study, it is aimed to analyze factors that cause Coronary Artery Disease using Random Forests Classifier. According to the analysis, we observed correct classification ratio and performance measure that creates susceptibility to Coronary Artery Disease for each factor. The performance measure results clearly show the impact of demographic characteristics on CAD. Additionally, this study shows that random forests algorithm can be used to the processing and classification of medical data such as CAD.

Chapter 31 – Keratoconus Disease and Three-Dimensional Simulation of the Cornea throughout the Process of Cross-Linking Treatment

Keratoconus is a corneal disease characterized by the progressive thinning and tapering of the cornea. Vision gradually decreases as the sphere-shaped cornea becomes more tapered and conical. With corneal cross-linking treatment, which increases the number of cross-links in the connective tissues of the corneal layers, the cornea hardens. The purpose of this chapter is to described the changes in the cornea between the processes before and after the cross-linking treatment. In this study, we used the Cropped Quad-Tree method as the cropping algorithm, Multilayer Perceptron and Logistic Regression methods to prepare the data set, and three-dimensional (3D) imaging methods to model the images in 3D form. With this application, it can be possible to follow up the healing process after the treatment and also monitor whether the treatment has achieved the desired results. This system was developed in order to support eye specialists in the disease diagnosis, treatment, and follow-up stages. It can be seen that the follow-up process of the disease by analyzing two-dimensional (2D) corneal images can be improved by using 3D images.

GPU efficient SAR image despeckling using mixed norms

Speckle noise which is inherent to Synthetic Aperture Radar (SAR) imaging obstructs various image exploitation tasks such as edge detection, segmentation, change detection, and target recognition. Therefore, speckle reduction is generally used as a first step which has to smooth out homogeneous regions while preserving edges and point scatterers. Traditional speckle reduction methods are fast and their memory consumption is insignificant. However, they are either good at smoothing homogeneous regions or preserving edges and point scatterers. State of the art despeckling methods are proposed to overcome this trade-off. However, they introduce another trade-off between denoising quality and resource consumption, thereby higher denoising quality requires higher computational load and/or memory consumption. In this paper, a local pixel-based total variation (TV) approach is proposed, which combines l2-norm and l1-norm in order to improve despeckling quality while keeping execution times reasonably short. Pixel-based approach allows efficient computation model with relatively low memory consumption. Their parallel implementations are also more efficient comparing to global TV approaches which generally require numerical solution of sparse linear systems. However, pixel-based approaches are trapped to local minima frequently hence despeckling quality is worse comparing to global TV approaches. Proposed method, namely mixed norm despeckling (MND), combines l2-norm and l1-norm in order to improve despeckling performance by alleviating local minima problem. All steps of the MND are parallelized using OpenMP on CPU and CUDA on GPU. Speckle reduction performance, execution time and memory consumption of the proposed method are shown using synthetic images and TerraSAR-X spot mode SAR images.