Simple hybrid method for fine microaneurysm detection from non-dilated diabetic retinopathy retinal images.

Microaneurysms detection is an important task in computer aided diagnosis of diabetic retinopathy. Microaneurysms are the first clinical sign of diabetic retinopathy, a major cause of vision loss in diabetic patients. Early microaneurysm detection can help reduce the incidence of blindness. Automatic detection of microaneurysms is still an open problem due to their tiny sizes, low contrast and also similarity with blood vessels. It is particularly very difficult to detect fine microaneurysms, especially from non-dilated pupils and that is the goal of this paper. Simple yet effective methods are used. They are coarse segmentation using mathematic morphology and fine segmentation using naive Bayes classifier. A total of 18 microaneurysms features are proposed in this paper and they are extracted for naive Bayes classifier. The detected microaneurysms are validated by comparing at pixel level with ophthalmologists' hand-drawn ground-truth. The sensitivity, specificity, precision and accuracy are 85.68, 99.99, 83.34 and 99.99%, respectively.

An ensemble classification-based approach applied to retinal blood vessel segmentation.

This paper presents a new supervised method for segmentation of blood vessels in retinal photographs. This method uses an ensemble system of bagged and boosted decision trees and utilizes a feature vector based on the orientation analysis of gradient vector field, morphological transformation, line strength measures, and Gabor filter responses. The feature vector encodes information to handle the healthy as well as the pathological retinal image. The method is evaluated on the publicly available DRIVE and STARE databases, frequently used for this purpose and also on a new public retinal vessel reference dataset CHASE_DB1 which is a subset of retinal images of multiethnic children from the Child Heart and Health Study in England (CHASE) dataset. The performance of the ensemble system is evaluated in detail and the incurred accuracy, speed, robustness, and simplicity make the algorithm a suitable tool for automated retinal image analysis.

Parameter-free optic disc detection.

The paper presents a simple, parameter-free method to detect the optic disc in retinal images. It works efficiently for blurred and noisy images with a varying ratio OD/image size. The method works equally well on images with different characteristics which often cause standard methods to fail or require a new round of training. The proposed method has been tested on 214 infant and adult retinal images and has been compared against hand-drawn ground truths generated by experts. It displays consistently high OD detection rates without any prior training or adjustment of the parameters.

Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering.

Exudates are the primary sign of Diabetic Retinopathy. Early detection can potentially reduce the risk of blindness. An automatic method to detect exudates from low-contrast digital images of retinopathy patients with non-dilated pupils using a Fuzzy C-Means (FCM) clustering is proposed. Contrast enhancement preprocessing is applied before four features, namely intensity, standard deviation on intensity, hue and a number of edge pixels, are extracted to supply as input parameters to coarse segmentation using FCM clustering method. The first result is then fine-tuned with morphological techniques. The detection results are validated by comparing with expert ophthalmologists' hand-drawn ground-truths. Sensitivity, specificity, positive predictive value (PPV), positive likelihood ratio (PLR) and accuracy are used to evaluate overall performance. It is found that the proposed method detects exudates successfully with sensitivity, specificity, PPV, PLR and accuracy of 87.28%, 99.24%, 42.77%, 224.26 and 99.11%, respectively.

Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods.

Diabetic retinopathy is a complication of diabetes that is caused by changes in the blood vessels of the retina. The symptoms can blur or distort the patient's vision and are a main cause of blindness. Exudates are one of the primary signs of diabetic retinopathy. Detection of exudates by ophthalmologists normally requires pupil dilation using a chemical solution which takes time and affects patients. This paper investigates and proposes a set of optimally adjusted morphological operators to be used for exudate detection on diabetic retinopathy patients' non-dilated pupil and low-contrast images. These automatically detected exudates are validated by comparing with expert ophthalmologists' hand-drawn ground-truths. The results are successful and the sensitivity and specificity for our exudate detection is 80% and 99.5%, respectively.

Thailand mutation and variation database (ThaiMUT).

With the completion of the human genome project, novel sequencing and genotyping technologies had been utilized to detect mutations. Such mutations have continually been produced at exponential rate by researchers in various communities. Based on the population's mutation spectra, occurrences of Mendelian diseases are different across ethnic groups. A proportion of Mendelian diseases can be observed in some countries at higher rates than others. Recognizing the importance of mutation effects in Thailand, we established a National and Ethnic Mutation Database (NEMDB) for Thai people. This database, named Thailand Mutation and Variation database (ThaiMUT), offers a web-based access to genetic mutation and variation information in Thai population. This NEMDB initiative is an important informatics tool for both research and clinical purposes to retrieve and deposit human variation data. The mutation data cataloged in ThaiMUT database were derived from journal articles available in PubMed and local publications. In addition to collected mutation data, ThaiMUT also records genetic polymorphisms located in drug related genes. ThaiMUT could then provide useful information for clinical mutation screening services for Mendelian diseases and pharmacogenomic researches. ThaiMUT can be publicly accessed from http://gi.biotec.or.th/thaimut.

Automatic extraction of the structure of the retinal blood vessel network of premature infants.

OBJECTIVE: Automatically detect the structure of blood vessels in ROP infants to allow ophthalmologist to analyze and detect the symptom early. MATERIAL AND METHOD: This study presents a set of methods for detection of the skeletonized structure of premature infant's low-contrast retinal blood vessel network. Steps has been optimized for this study, namely statistically optimized LOG edge detection filter, Otsu thresholding, Medial Axis transform skeletonization, pruning, and edge thinning. RESULTS: A set of 100 test images are grouped together into five testing groups based on their similar characteristics and clinicians suggestions. The authors applied the series of methods proposed on all the 100 images. The result from the algorithm was compared with ophthalmologists' hand-drawn ground truth and it can detect the blood vessel with a high specificity of 0.9879 and sensitivity of 0.8935. CONCLUSION: The authors' algorithm can detect blood vessels effectively even though the image quality may not be good, have high noise, and low contrast. The algorithm can also detect the blood vessel at important locations such as the edge of the retina.