# Efficient and Accurate Hemorrhages Detection in Retinal Fundus Images Using Smart Window Features
## 1 Introduction
### Related Work  
Several methods [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22] were proposed in the literature for retinal image segmentation. Reference [10] provides a detailed review of retinal blood vessel segmentation. Several empirical and machine learning-based image processing approaches were employed for hemorrhage segmentation. Reference [13] is a survey of recent developments in automatic detection of DR. References [14; 15; 16] used the k-Nearest neighbor algorithm to cluster lesions in fundus images. [PERSON] et al. [17] and [PERSON] et al. [18] used a support vector machine to categorize the performance prowess of different classifiers. Although these methods produce good results, they evaluated their output based on lesion and image.  
[PERSON] et al. [11] used a convolutional neural network (CNN) for hemorrhage segmentation. This method preprocesses, trains, refines the data, and finally segments with CNN. The technique consists of the coarsely annotated bounding box for each hemorrhage. The box size is increased at random and accompanied by hemorrhage. Next, a refining network is used to capture the data from the bounding box procedure. Finally, CNN is applied for the automatic detection of hemorrhages. Although this method gives accurate results, they require a large amount of training data for effective segmentation. [PERSON] et al. [12] combined edge detection, morphology, and connected components for the segmentation of blood vessels and hemorrhages. This method involves segmentation and classification stages. In the segmentation stage, blood vessels and lesions are extracted from the fundus image, and then the local binary features are extracted. Finally, the Naive Bayesian classifier is used to discriminate between hemorrhage and non-hemorrhage. This method may produce a high false-positive rate (common to edge segmentation approaches). [PERSON] et al. [20] segmented hemorrhages using the splat feature. First, the images are segmented into different partitions (called splats). Then the watershed algorithm is used to extract boundaries. Next, pixels are grouped by an irregular grid algorithm and are estimated with boundaries from the magnitude of the gradient. [PERSON] et al. [21] used a Frangi-based filter for red lesion detection in retinal fundus images, while [PERSON] et al. [22] used mathematical morphology to detect non-proliferative diabetic retinopathy.  
Most of these studies evaluate their output based on lesion or image. Output evaluation by image count the number of lesion or image and then find the sensitivity or specificity of the output. This paper proposes a different output evaluation method. Unlike the several existing literature, we evaluate output based on the pixel. It is believed that output evaluation using pixels instead of lesion/images provides a clearer assessment when detecting retinal images [19]. Our method detects hemorrhages (NPDR or PDR) from retinal fundus images.  
Furthermore, previous algorithms perform segmentation of non-hemorrhage candidate regions based on estimations of morphology or connected component analysis. They reduce the false-positive rate with the formation of hemorrhages using the concepts of any irregular shape. The proposed method, unlike previous methods, considers all candidates for analysis and detection.  
The paper is organized as follows. The fundamental concepts and proposed method are reported in Section 2. The results of the experiment are illustrated in Section 3. Finally, a discussion is given in Section 4, and the paper is concluded in Section 5.