Retinal imaging analysis based on vessel detection.

With an increase in the advancement of digital imaging and computing power, computationally intelligent technologies are in high demand to be used in ophthalmology cure and treatment. In current research, Retina Image Analysis (RIA) is developed for optometrist at Eye Care Center in Management and Science University. This research aims to analyze the retina through vessel detection. The RIA assists in the analysis of the retinal images and specialists are served with various options like saving, processing and analyzing retinal images through its advanced interface layout. Additionally, RIA assists in the selection process of vessel segment; processing these vessels by calculating its diameter, standard deviation, length, and displaying detected vessel on the retina. The Agile Unified Process is adopted as the methodology in developing this research. To conclude, Retina Image Analysis might help the optometrist to get better understanding in analyzing the patient's retina. Finally, the Retina Image Analysis procedure is developed using MATLAB (R2011b). Promising results are attained that are comparable in the state of art.

Automated nuclei segmentation of malignant using level sets.

Segmentation of objects from a noisy and complex image is still a challenging task that needs to be addressed. This article proposed a new method to detect and segment nuclei to determine whether they are malignant or not (determination of the region of interest, noise removal, enhance the image, candidate detection is employed on the centroid transform to evaluate the centroid of each object, the level set [LS] is applied to segment the nuclei). The proposed method consists of three main stages: preprocessing, seed detection, and segmentation. Preprocessing stage involves the preparation of the image conditions to ensure that they meet the segmentation requirements. Seed detection detects the seed point to be used in the segmentation stage, which refers to the process of segmenting the nuclei using the LS method. In this research work, 58 H&E breast cancer images from the UCSB Bio-Segmentation Benchmark dataset are evaluated. The proposed method reveals the high performance and accuracy in comparison to the techniques reported in literature. The experimental results are also harmonized with the ground truth images.