Detection of Choroidal Neovascularization by Optical Coherence Tomography Angiography with Assistance from Use of the Image Segmentation Method

Abstract

Optical coherence tomography angiography (OCTA) is a popular medical imaging technology that can quickly establish a three-dimensional model of the fundus without dye injection. However the number of images in a model is quite large, so finding the lesions through image processing technology can greatly reduce the time required for the judgment of the condition. This paper proposes a method for finding choroidal neovascularization (CNV) in OCTA images. Among the several characteristics of CNV, the larger turning angle of blood vessels is a relatively clear feature, so we will use this property to find out whether there is CNV in an OCTA image. We will transform the color space to CIELAB space, and extract the L-channel prior to preceding to the next step. We will then use some image segmentation methods to find the clearer vessel region. Finally, we will detect the CNV through certain morphology methods. The experimental result shows that our proposed method can effectively find the CNV in the OCTA image, meaning that we can make automated judgments through this method in the future and reduce the time necessary for human judgment.

A new approach for sensitivity improvement of retinal blood vessel segmentation in high-resolution fundus images based on phase stretch transform

“…This method determines the threshold that maximizes the distinctness of the two populations into which the image is divided. This distinctiveness is represented by the interclass variance [31] [32].…”

Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook

“…Global thresholding determines one threshold value for the entire image frame and is determined by an analysis of the whole image intensity histogram. The Otsu method [17] is a commonly used automatic thresholding technique for OCTA images [18][19][20][21][22][23] and is based on finding a threshold that minimizes the intraclass variance of the thresholded black and white pixels. Other global thresholding methods are based on finding a specific percentile of the image intensity histogram [24], the progressive weighted mean of the image intensity histogram [25,26], or by simply fine-tuning a specific gray level [27].…”

A Complete Review of Automatic Detection, Segmentation, and Quantification of Neovascularization in Optical Coherence Tomography Angiography Images

“…One of the major challenges faced in this research area is the complexity of images. Most techniques struggle with low image quality, as well as noise and background patterns that resemble NV intensity, NV patterns, and scattered artifacts [23][24][25][26][27][28][29]. These issues often lead to over-segmentation and under-segmentation, resulting in low precision and recall, especially in CNV cases [30].…”

“…A vascular network extraction method with partial line detection was used for NV of curvilinear structures [60]. Binarization and morphological analysis were applied to the preprocessed image in the works of Cheng et al [27], Coscas et al [65], and Zhang et al [69]. Then, thresholding by the size of connected components was carried out in the works of Cheng et al [27] and Coscas et al [65] for the NV that is prominent by size.…”

“…Binarization and morphological analysis were applied to the preprocessed image in the works of Cheng et al [27], Coscas et al [65], and Zhang et al [69]. Then, thresholding by the size of connected components was carried out in the works of Cheng et al [27] and Coscas et al [65] for the NV that is prominent by size. Eccentricity features were extracted for each candidate region in the work of Cheng et al [27].…”