Improved fuzzy C-means clustering in the process of exudates detection using mathematical morphology

Recent trends and advances in fundus image analysis: A review

Automated retinal image analysis holds prime significance in the accurate diagnosis of various critical eye diseases that include diabetic retinopathy (DR), age-related macular degeneration (AMD), atherosclerosis, and glaucoma. Manual diagnosis of retinal diseases by ophthalmologists takes time, effort, and financial resources, and is prone to error, in comparison to computer-aided diagnosis systems. In this context, robust classification and segmentation of retinal images are primary operations that aid clinicians in the early screening of patients to ensure the prevention and/or treatment of these diseases. This paper conducts an extensive review of the state-of-the-art methods for the detection and segmentation of retinal image features. Existing notable techniques for the detection of retinal features are categorized into essential groups and compared in depth. Additionally, a summary of quantifiable performance measures for various important stages of retinal image analysis, such as image acquisition and preprocessing, is provided. Finally, the widely used in the literature datasets for analyzing retinal images are described and their significance is emphasized.

Bibliometric analysis of patent infringement retrieval model based on self-organizing map neural network algorithm

Purpose

The purpose of this paper is to quickly retrieve the same or similar patents in a large patent database.

Design/methodology/approach

The research is carried out through the analysis of the issue of patent examination, the type of patent infringement search and theories related to patent infringement determination and text mining.

Findings

The results show that the model improves the speed of patent search. It can quickly, accurately and comprehensively retrieve the same or equivalent patents as the imported patent claims.

Research limitations/implications

The patent infringement detection mainly focuses on the measurement of patent similarity in the implementation method. It is not mature, and there is still much room for improvement in research.

Practical implications

The model improves the efficiency of patent infringement detection, increases the accuracy of detection and protects the interests of patent stakeholders.

Originality/value

This study has great significance for improving the efficiency of patent examiners.

Optimizing the trainable B-COSFIRE filter for retinal blood vessel segmentation

Segmentation of the retinal blood vessels using filtering techniques is a widely used step in the development of an automated system for diagnostic retinal image analysis. This paper optimized the blood vessel segmentation, by extending the trainable B-COSFIRE filter via identification of more optimal parameters. The filter parameters are introduced using an optimization procedure to three public datasets (STARE, DRIVE, and CHASE-DB1). The suggested approach considers analyzing thresholding parameters selection followed by application of background artifacts removal techniques. The approach results are better than the other state of the art methods used for vessel segmentation. ANOVA analysis technique is also used to identify the most significant parameters that are impacting the performance results (p-value ¡ 0.05). The proposed enhancement has improved the vessel segmentation accuracy in DRIVE, STARE and CHASE-DB1 to 95.47, 95.30 and 95.30, respectively.