Toufique Ahmed Soomro

Contrast normalization steps for increased sensitivity of a retinal image segmentation method

Retinal vessel segmentation plays a major role in the detection of many eye diseases, and it is required to implement an automated algorithm for analyzing the progress of eye diseases. A variety of automated segmentation methods have been presented but almost all studies to date showed weakness in their low sensitivity toward narrow low-contrast vessels. A new segmentation method is proposed to address the issue of low sensitivity, by including modules such as principal component analysis-based color-to-gray conversion, scale normalization factors for improved narrow vessel detection, anisotropic diffusion filtering with an adequate stopping rule, and edge pixel-based hysteresis threshold. The impact of these additional steps is assessed on publicly available databases like DRIVE and STARE. For the case of DRIVE database, the sensitivity is raised from 73 to 75%, while maintaining the accuracy of 96.5%, and found to provide evidence of improved sensitivity.

Automatic retinal vessel extraction algorithm based on contrast-sensitive schemes

Retinal vessel segmentation plays a key role in the detection of numerous eye diseases, and its reliable computerised implementation becomes important for automatic retinal disease screening systems. A large number of retinal vessel segmentation algorithms have been reported, primarily based on three main steps including making the background uniform, second-order Gaussian detector application and finally the region-grown binarization. Although these methods improve the accuracy levels, their sensitivity to low-contrast vessels still needs attention. In this paper, some contrast-sensitive approaches are discussed that once embedded in the conventional algorithm results in improved sensitivity for a given retinal vessel extraction technique. The impact of these add-on modules is assessed on publicly available databases like DRIVE and STARE and found to provide promising results.

Computerised approaches for the detection of diabetic retinopathy using retinal fundus images: a survey

AbstractEye-related disease such as diabetic retinopathy (DR) is a medical ailment in which the retina of the human eye is smashed because of damage to the tiny retinal blood vessels in the retina. Ophthalmologists identify DR based on various features such as the blood vessels, textures and pathologies. With the rapid development of methods of analysis of biomedical images and advanced computing techniques, image processing-based software for the detection of eye disease has been widely used as an important tool by ophthalmologists. In particular, computer vision-based methods are growing rapidly in the field of medical images analysis and are appropriate to advance ophthalmology. These tools depend entirely on visual analysis to identify abnormalities in Retinal Fundus images. During the past two decades, exciting improvement in the development of DR detection computerised systems has been observed. This paper reviews the development of analysing retinal images for the detection of DR in three aspects: automatic algorithms (classification or pixel to pixel methods), detection methods of pathologies from retinal fundus images, and extraction of blood vessels of retinal fundus image algorithms for the detection of DR. The paper presents a detailed explanation of each problem with respect to retinal images. The current techniques that are used to analyse retinal images and DR detection issues are also discussed in detail and recommendations are made for some future directions.

Automatic Retinal Vessel Extraction Algorithm

Retinal vessel segmentation plays a key role in the detection of numerous eye diseases, and its reliable computerised implementation becomes important for automatic retinal disease screening systems. A large number of retinal vessel segmentation algorithms have been reported, primarily based on three main steps including uniforming background, using the second-order Gaussian detector and applying binarization. These methods though improve the accuracy levels, their sensitivity to low-contrast in vessels still needs attention. In this paper, some contrast-sensitive approaches are discussed and embedded in the conventional algorithms, resulting in improved sensitivity for a given retinal vessel extraction technique. The proposed method gives good performance on both publicly databases with the accurate vessel extraction on STARE database. The proposed unsupervised method achieves the accuracy of 94.41%, much better than some existing unsupervised methods and comparable to some supervised methods. Its efficiency with different image conditions, together with its simplicity and fast operation, makes the blood vessel segmentation application suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.

Role of Image Contrast Enhancement Technique for Ophthalmologist as Diagnostic Tool for Diabetic Retinopathy

Analysing the retinal colour fundus is a critical step before any proposed computerised automatic detection of eye disease, especially Diabetic Retinopathy (DR). The retinal colour fundus image contains noise and varying low contrast of the blood vessel against its surrounding background. It makes it difficult to analyse the proper order of the vessels network for detecting DR disease progress. The invasive method Fluorescein Angiogram Fundus (FFA) resolves these problems, but is not recommended due to an agent injection that leads to other side effects on the patients health, in the worst cases death. In this research work, we propose a new image enhancement method based on a morphological operation along with proposed threshold based stationary wavelet transform for retinal fundus images and Contrast Limited Adaptive Histogram Equalisation (CLAHE) for the vessel enhancement. The experimental results show much better results than the FFA images. Experimental results are based on three databases of retinal colour fundus images and FFA images. The performance is evaluated by measuring the contrast enhancement factor of retinal colour fundus images and FFA images. The results show that the proposed image enhancement method is superior to other non-invasive image enhancement methods as well as invasive methods, thus it will play an important role in imaging retinal blood vessels. An average contrast improvement factor of 5.63 on colour fundus images is achieved as well as 5.57 on FFA images. This significant contribution to the enhancement of the contrast of retinal colour fundus will be one primary tool to reduce the use of an invasive method.

Study of the Noise Level in the Colour Fundus Images

Diabetic Retinopathy DR causes vision loss insufficiency due to impediment rising from high sugar level conditions disturbing the retina. The Progression of DR occurs in the Foveal avascular zone FAZ due to loss of tiny blood vessels of capillary network. Due to image acquisition process of fundus camera, the colour retinal fundus image suffers from varying contrast and noise problems. To overcome varying contrast and noise problem in fundus image, the technique has been implemented. The technique is contained on the Retinex algorithm along with stationary wavelet transform. The technique has been applied on 36 high resolution fundus HRF image database contain the 18 bad quality images and 18 good quality images. The RETSWT RETinex and Stationary Wavelet Transform developed with introduces denoising techniques. Stationary wavelet transform is used as denoised technique. RETSWT achieved the average PSNR improvement of 2.39i¾źdb good quality images else it achieved the average PSNR improvement of 2.20i¾źdb in the bad quality images. The RETSWT image enhancement method potentially reduces the need of the invasive fluorescein angiogram in DR assessment.

A generalized multi-scale line-detection method to boost retinal vessel segmentation sensitivity

Many chronic eye diseases can be conveniently investigated by observing structural changes in retinal blood vessel diameters. However, detecting changes in an accurate manner in face of interfering pathologies is a challenging task. The task is generally performed through an automatic computerized process. The literature shows that powerful methods have already been proposed to identify vessels in retinal images. Though a significant progress has been achieved toward methods to separate blood vessels from the uneven background, the methods still lack the necessary sensitivity to segment fine vessels. Recently, a multi-scale line-detector method proved its worth in segmenting thin vessels. This paper presents modifications to boost the sensitivity of this multi-scale line detector. First, a varying window size with line-detector mask is suggested to detect small vessels. Second, external orientations are fed to steer the multi-scale line detectors into alignment with flow directions. Third, optimal weights are suggested for weighted linear combinations of individual line-detector responses. Fourth, instead of using one global threshold, a hysteresis threshold is proposed to find a connected vessel tree. The overall impact of these modifications is a large improvement in noise removal capability of the conventional multi-scale line-detector method while finding more of the thin vessels. The contrast-sensitive steps are validated using a publicly available database and show considerable promise for the suggested strategy.

Boosting Sensitivity of a Retinal Vessel Segmentation Algorithm with Convolutional Neural Network

Accurate vessel segmentation is a tough task for various medical images applications especially the segmentation of retinal images vessels. A computerised algorithm is required for analysing the progress of eye diseases. A variety of computerised retinal segmentation methods have been proposed but almost all methods to date show low sensitivity for narrowly low contrast vessels. We propose a new retinal vessel segmentation algorithm to address the issue of low sensitivity. The proposed method introduces a deep learning model along with pre-processing and post-processing. The pre-processing is used to handle the issue of uneven illuminations. We design a fully Convolutional Neural Network (CNN) and train it to get fine vessels observation.The post-processing step is used to remove the background noise pixels to achieve well-segmented vessels. The proposed segmentation method gives good segmented images especially for detecting tiny vessels. We evaluate our method on the commonly used publicly available databases: DRIVE and STARE databases. The higher sensitivity of 75% leads to proper detection of tiny vessels with an accuracy of 94.7%.

Boosting sensitivity of a retinal vessel segmentation algorithm

The correlation between retinal vessel structural changes and the progression of diseases such as diabetes, hypertension, and cardiovascular problems has been the subject of several large-scale clinical studies. However, detecting structural changes in retinal vessels in a sufficiently fast and accurate manner, in the face of interfering pathologies, is a challenging task. This significantly limits the application of these studies to clinical practice. Though monumental work has already been proposed to extract vessels in retinal images, they mostly lack necessary sensitivity to pick low-contrast vessels. This paper presents a couple of contrast-sensitive measures to boost the sensitivity of existing retinal vessel segmentation algorithms. Firstly, a contrast normalization procedure for the vascular structure is adapted to lift low-contrast vessels to make them at par in comparison with their high-contrast counterparts. The second measure is to apply a scale-normalized detector that captures vessels regardless of their sizes. Thirdly, a flood-filled reconstruction strategy is adopted to get binary output. The process needs initialization with properly located seeds, generated here by another contrast-sensitive detector called isophote curvature. The final sensitivity boosting measure is an adoption process of binary fusion of two entirely different binary outputs due to two different illumination correction mechanism employed in the earlier processing stages. This results in improving the noise removal capability while picking low-contrast vessels. The contrast-sensitive steps are validated on a publicly available database, which shows considerable promise in the strategy adopted in this research work.

Neural network based denoised methods for retinal fundus images and MRI brain images

Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging techniques are used to image the inner portion of human body for medical diagnosis. In this research work, retinal colour fundus images and MRI brain images noise level has been improved. Fundus Fluorescein Angiography (FFA) is the invasive based technique used to give high contrast retinal images but it used contrast injection and other side Magnetic Resonance Imaging (MRI) is a medical used to produce the high contrast image. The biomedical images are mostly suffered from the varied contrast and due to varied contrast, the details of images are not observed properly even after the image enhancement techniques because the presence of noise. In this research, The High-Resolution Fundus (HRF) database is used and it contained 36 images of two pairs (18 good quality images and 18 bad quality images). Oasis MRI brain image database is also used and it contained 30 images. Radial Basis Function (RBF) neural network gave highest PSNR improvement of 53% and 56% in HRF retinal images database and Oasis MRI Brain images database as compared to wavelet technique (18%,35%) and sub space method( 29%,9%). The optimal denoised method is one important step to get better result of contrast normalisation techniques and give accurate results to diagnose the disease progress.