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Diabetic retinopathy (DR) is an important retinal disease threatening people with the long diabetic history. Blood leakage in retina leads to the formation of red lesions in retina the analysis of which is helpful in the determination of severity of disease. In this paper, a novel red-lesion extraction method is proposed. The new method firstly determines the boundary pixels of blood vessel and red lesions. Then, it determines the distinguishing features of boundary pixels of red-lesions to discriminate them from other boundary pixels. The main point utilized here is that a red lesion can be observed as significant intensity changes in almost all directions in the fundus image. This can be feasible through considering special neighborhood windows around the extracted boundary pixels. The performance of the proposed method has been evaluated for three different datasets including Diaretdb0, Diaretdb1 and Kaggle datasets. It is shown that the method is capable of providing the values of 0.87 and 0.88 for sensitivity and specificity of Diaretdb1, 0.89 and 0.9 for sensitivity and specificity of Diaretdb0, 0.82 and 0.9 for sensitivity and specificity of Kaggle. Also, the proposed method has a time-efficient performance in the red-lesion extraction process.

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Cognitive Radio Sensor Networks (CRSNs) are wireless sensor networks implemented with cognitive radio capability to provide spectrum resources for sensors in an opportunistic manner. The application of CRSNs can be found in the paradigm of Internet of Things (IoT) where each sensor represents an object in the IoT paradigm. In order to prevent simultaneous transmissions of Primary Users (PUs) and CRSN nodes, sensors should be aware of the presence of PUs. One of the most common methods for making the sensors aware of the presence of PUs is consecutive spectrum sensing. Since the sensors have the duty of both sensing environment and transmitting the results, their battery lives are highly prone to rapid drain. In addition, the lack of effective management of sensor operations for spectrum sensing may lead to short longevity of the network. In this paper, the energy consumption process of sensors in such networks has been analyzed and an energy‐based algorithm is proposed to appropriately select sensors based on their detection probabilities for spectrum sensing. The analytical and numerical results confirm the improved lifetime and performance based on our proposed algorithm in comparison with the conventional ones. Moreover, the computational complexity of the proposed algorithm is significantly lower than that of the existing methods.