OBJECTIVE: To propose methods for computer grading of the severity of 3 early lesions, namely, hemorrhages and microaneurysms, hard exudates, and cotton-wool spots, and classification of nonproliferative diabetic retinopathy (NPDR) based on these 3 types of lesions. METHODS: Using a computer diagnostic system developed earlier, the number of each of the 3 early lesions and the size of each lesion in the standard photographs were determined. Computer classification criteria were developed for the levels of individual lesions and for NPDR. Evaluation of the criteria was performed using 430 fundus images with normal retinas or any degree of retinopathy and 361 fundus images with no retinopathy or the 3 early lesions only. The results were compared with those of the graders at the University of Wisconsin Ocular Epidemiology Reading Center and an ophthalmologist. MAIN OUTCOME MEASURES: Agreement rates in the classification of NPDR between the computer system and human experts. RESULTS: In determining the severity levels of individual lesions, the agreement rates between the computer system and the reading center were 82.6%, 82.6%, and 88.3% using the 430 images and 85.3%, 87.5%, and 93.1% using the 361 images, respectively, for hemorrhages and microaneurysms, hard exudates, and cotton-wool spots. When the "questionable" category was excluded, the corresponding agreement rates were 86.5%, 92.3%, and 91.0% using the 430 images and 89.7%, 96.3%, and 97.4% using the 361 images. In classifying NPDR, the agreement rates between the computer system and the ophthalmologist were 81.7% using the 430 images and 83.5% using the 361 images. CONCLUSIONS: The proposed criteria for computer classification produced results that are comparable with those provided by human experts. With additional research, this computer system could become a useful clinical aid to physicians and a tool for screening, diagnosing, and classifying NPDR.
OBJECTIVE: To propose methods for computer grading of the severity of 3 early lesions, namely, hemorrhages and microaneurysms, hard exudates, and cotton-wool spots, and classification of nonproliferative diabetic retinopathy (NPDR) based on these 3 types of lesions. METHODS: Using a computer diagnostic system developed earlier, the number of each of the 3 early lesions and the size of each lesion in the standard photographs were determined. Computer classification criteria were developed for the levels of individual lesions and for NPDR. Evaluation of the criteria was performed using 430 fundus images with normal retinas or any degree of retinopathy and 361 fundus images with no retinopathy or the 3 early lesions only. The results were compared with those of the graders at the University of Wisconsin Ocular Epidemiology Reading Center and an ophthalmologist. MAIN OUTCOME MEASURES: Agreement rates in the classification of NPDR between the computer system and human experts. RESULTS: In determining the severity levels of individual lesions, the agreement rates between the computer system and the reading center were 82.6%, 82.6%, and 88.3% using the 430 images and 85.3%, 87.5%, and 93.1% using the 361 images, respectively, for hemorrhages and microaneurysms, hard exudates, and cotton-wool spots. When the "questionable" category was excluded, the corresponding agreement rates were 86.5%, 92.3%, and 91.0% using the 430 images and 89.7%, 96.3%, and 97.4% using the 361 images. In classifying NPDR, the agreement rates between the computer system and the ophthalmologist were 81.7% using the 430 images and 83.5% using the 361 images. CONCLUSIONS: The proposed criteria for computer classification produced results that are comparable with those provided by human experts. With additional research, this computer system could become a useful clinical aid to physicians and a tool for screening, diagnosing, and classifying NPDR.
Authors: Carla Agurto; Victor Murray; Eduardo Barriga; Sergio Murillo; Marios Pattichis; Herbert Davis; Stephen Russell; Michael Abramoff; Peter Soliz Journal: IEEE Trans Med Imaging Date: 2010-02 Impact factor: 10.048
Authors: Zhitao Xiao; Xinpeng Zhang; Lei Geng; Fang Zhang; Jun Wu; Jun Tong; Philip O Ogunbona; Chunyan Shan Journal: Biomed Eng Online Date: 2017-10-26 Impact factor: 2.819