# Efficient and Accurate Hemorrhages Detection in Retinal Fundus Images Using Smart Window Features
## 1 Introduction  
The world health organization (WHO) estimates Diabetic retinopathy (DR) as the fifth leading cause of visual impairment and the fourth cause of blindness in the world. Globally, 800 million people have myopia, hypermetropia, presbyopia. Out of this, 100 million have moderate-to-severe distance vision blindness [1]. The increasing number of individuals suffering from diabetes mellitus (DM) has made the number of DR patients rise inevitably. Factors responsible for the prevalence of DM include obesity, sedentary lifestyle, physical inactivity, and lack of awareness [2]. Early detection and prevention are important steps to avoid blindness from DR disease. Effective control of vision loss includes regular eye examination and management of risk factors (such as glycemic, hypertension, and hyperlipidemia) [3, 4]. The American Diabetes Association (ADA) recommends that type 1 diabetes patients be screened three to five years after onset, and type 2 diabetic patients be screened one year after onset [5, 6]. Abnormalities in DR are categorized as non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR). NPDR disease is the earliest stage of DR that changes the blood vessel of the eye. The changes in NPDR do not present any symptoms and are not visible to the naked eye. During the later stage of pathology, microaneuryss turn into hemorrhages.  
In the past decades, eye examinations were performed sequentially. The best-corrected vision is first located, and then a slit-lamp microscope obtains the pressure of the eye. Next, the ophthalmoscopy is used to judge the entire retinal (macular area, retinal periphery, andora Serrata). This procedure requires the judgment of an experienced ophthalmologist [7].  
Recently, refined techniques such as digital retinal imaging, fundus photographs, and fluorescein angiography are popular methods to perform retinal examination [8]. However, this does not replace the primary examination of the eye. The new modalities advance the field of ophthalmology; hence they act as the second interpreter for the ophthalmologist to make findings plausible. Regular eye examination reduces the risk of permanent vision loss and alerts many people of serious health problems (such as high blood pressure, high cholesterol, diabetes, and cancer). Retinal imaging produces a clearer view into the eye for the doctor to see any early signs of health conditions [9]. Despite the numerous advantages associated with retinal imaging, they are often blurry, poorly illuminated, and have a narrow field of view. The characteristics of the hemorrhages are similar to some of the dark regions caused by lighting conditions and the blood vessels; hence segmenting hemorrhages in retinal images becomes difficult. Figure 1 is an example of retinal images.  
The problems of retinal images necessitated an algorithm that can segment hemorrhages efficiently and accurately. The proposed algorithm first enhances the contrast of the fundus image with the adaptive histogram equalization. Then the edge information manipulates the intensity transformation adaptively to make the hemorrhage appear more prominently. Next, a fuzzy-logic-based filter is used to sharpen the images. Finally, the candidate image is extracted, segmented, and classified. The major contributions of this paper are summarized as follows:  
Figure 1: Example of retinal images, (**a**,**b**) dark images, (**c**,**d**) uneven illumination of fundus images.  
A method to overcome the problem of blurriness and to distinguish hemorrhages from the blood vessels;  
A preprocessing and candidate extraction method for hemorrhage detection;  
A smart window-based feature extraction procedure for segmentation of hemorrhages.