Retinal status analysis method based on feature extraction and quantitative grading in OCT images.

BACKGROUND: Optical coherence tomography (OCT) is widely used in ophthalmology for viewing the morphology of the retina, which is important for disease detection and assessing therapeutic effect. The diagnosis of retinal diseases is based primarily on the subjective analysis of OCT images by trained ophthalmologists. This paper describes an OCT images automatic analysis method for computer-aided disease diagnosis and it is a critical part of the eye fundus diagnosis. METHODS: This study analyzed 300 OCT images acquired by Optovue Avanti RTVue XR (Optovue Corp., Fremont, CA). Firstly, the normal retinal reference model based on retinal boundaries was presented. Subsequently, two kinds of quantitative methods based on geometric features and morphological features were proposed. This paper put forward a retinal abnormal grading decision-making method which was used in actual analysis and evaluation of multiple OCT images. RESULTS: This paper showed detailed analysis process by four retinal OCT images with different abnormal degrees. The final grading results verified that the analysis method can distinguish abnormal severity and lesion regions. This paper presented the simulation of the 150 test images, where the results of analysis of retinal status showed that the sensitivity was 0.94 and specificity was 0.92.The proposed method can speed up diagnostic process and objectively evaluate the retinal status. CONCLUSIONS: This paper aims on studies of retinal status automatic analysis method based on feature extraction and quantitative grading in OCT images. The proposed method can obtain the parameters and the features that are associated with retinal morphology. Quantitative analysis and evaluation of these features are combined with reference model which can realize the target image abnormal judgment and provide a reference for disease diagnosis.

[Extracting target from blurred midwave infrared image based on immune template clustering].

Extracting targets from a blurred midwave infrared image is a challenging task due to the fuzziness of the image. Inspired by the coordination mechanism between biological innate immunity and adaptive immunity, an immune template clustering targets extraction method is proposed, which based on imaging mechanism and template statistical property of midwave image. Firstly, by learning from the recognition function of innate immunity and maximizing the between-cluster variance, a midwave blurred infrared image is segmented into a target pixel set, a background pixel set and a blurred pixel set. Secondly, according to the presentation function of innate immunity, the frequency domain template features of pixels in midwave blurred infrared image are extracted. Finally, adaptive immune clustering is completed for the blurred pixel set based on frequency domain template feature, in order to divide each blurred pixel into target pixel or background pixel. Experimental results show that the proposed algorithm can extract targets from a midwave blurred infrared image efficiently. Compared with classical edge template and conventional region template methods, the immune template clustering method has better extraction efficiency, absolute error rate and coincidence degree with ground truth.