CA : A Cancer Journal for Clinicians

Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

"Graefe's Archive" is a distinguished international journal that presents original clinical reports and clinically relevant experimental studies. Founded in 1854 by Albrecht von Graefe to serve as a source of useful clinical information and a stimulus for discussion, the journal has published articles by leading ophthalmologists and vision research scientists for more than a century. With peer review by an international Editorial Board and prompt English-language publication, "Graefe's Archive" provides rapid dissemination of clinical and clinically related experimental information.

Graefe's archive for clinical and experimental ophthalmology

Texture features for image classification and retrieval.

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Although retinal vessel segmentation has been extensively researched, a robust and time efficient segmentation method is highly needed. This paper presents a local adaptive thresholding technique based on gray level cooccurrence matrix- (GLCM-) energy information for retinal vessel segmentation. Different thresholds were computed using GLCM-energy information. An experimental evaluation on DRIVE database using the grayscale intensity and Green Channel of the retinal image demonstrates the high performance of the proposed local adaptive thresholding technique. The maximum average accuracy rates of 0.9511 and 0.9510 with maximum average sensitivity rates of 0.7650 and 0.7641 were achieved on DRIVE and STARE databases, respectively. When compared to the widely previously used techniques on the databases, the proposed adaptive thresholding technique is time efficient with a higher average sensitivity and average accuracy rates in the same range of very good specificity.

/pdf/adaptive-thresholding-technique-for-retinal-vessel-te90j0qnny.pdf

Adaptive Thresholding Technique for Retinal Vessel Segmentation Based on GLCM-Energy Information

Due to noise from uneven contrast and illumination during acquisition process of retinal fundus images, the use of efficient preprocessing techniques is highly desirable to produce good retinal vessel segmentation results. This paper develops and compares the performance of different vessel segmentation techniques based on global thresholding using phase congruency and contrast limited adaptive histogram equalization (CLAHE) for the preprocessing of the retinal images. The results obtained show that the combination of preprocessing technique, global thresholding, and postprocessing techniques must be carefully chosen to achieve a good segmentation performance.

https://downloads.hindawi.com/journals/cmmm/2015/895267.pdf

Comparative study of retinal vessel segmentation based on global thresholding techniques.

Global food security has become a very important research focus. This is due to the fact that food is a basic need of human beings and its adequate supply to meet the need of humans must be ensured. Plant diseases have, however, been one of the major problems threatening the adequate supply of food to humans. The early detection of these diseases can assist in their efficient management, thus making huge differences between survival and destruction of crops in farmlands affected by these plant diseases. Deep neural networks have been successfully applied in the field of artificial intelligence. This has inspired an increased research into the use of deep learning in the domains of image processing and computer vision. This paper present a study on the use of deep learning-based approach to identify diseased plants using leaf images by transfer learning. The study uses NASNet architeure for the convolutionary neural networks (CNN). The model is then trained and tested using a publicly available PlantVillage project dataset that contains varied images of plant leaves with multiple variations in infection status and location in the plants. Using the model, an accuracy rate of 93.82% was achieved.

Deep Learning Based on NASNet for Plant Disease Recognition Using Leave Images

As the use of robotic-assisted surgery systems continue to increase, highly accurate and timely efficient automatic vasculature detection techniques for large and thin vessels in the retinal images...

https://journals.sagepub.com/doi/pdf/10.5772/60581

Retinal Vessel Segmentation: A Comparative Study of Fuzzy C-means and Sum Entropy Information on Phase Congruency

As retinopathies continue to be major causes of visual loss and blindness worldwide, early detection and management of these diseases will help achieve significant reduction of blindness cases. However, an efficient automatic retinal vessel segmentation approach remains a challenge. Since efficient vessel network detection is a very important step needed in ophthalmology for reliable retinal vessel characterization, this paper presents study on the combination of difference image and K-means clustering for the segmentation of retinal vessels. Stationary points in the vessel center-lines are used to model the detection of twists in the vessel segments. The combination of arc-chord ratio with stationary points is used to compute tortuosity index. Experimental results show that the proposed K-means combined with difference image achieved a robust segmentation of retinal vessels. A maximum average accuracy of 0.9556 and a maximum average sensitivity of 0.7581 were achieved on DRIVE database while a maximum average accuracy of 0.9509 and a maximum average sensitivity of 0.7666 were achieved on STARE database. When compared with the previously proposed techniques on DRIVE and STARE databases, the proposed technique yields higher mean sensitivity and mean accuracy rates in the same range of very good specificity. In a related development, a non-normalized tortuosity index that combined distance metric and the vessel twist frequency proposed in this paper also achieved a strong correlation of 0.80 with the expert ground truth.

/pdf/automatic-retinal-vessel-detection-and-tortuosity-2nuo4wsisq.pdf

Temitope Mapayi

Papers

Adaptive Thresholding Technique for Retinal Vessel Segmentation Based on GLCM-Energy Information

Comparative study of retinal vessel segmentation based on global thresholding techniques.

Deep Learning Based on NASNet for Plant Disease Recognition Using Leave Images

Retinal Vessel Segmentation: A Comparative Study of Fuzzy C-means and Sum Entropy Information on Phase Congruency

Automatic retinal vessel detection and tortuosity measurement