Sparse dissimilarity constrained coding for glaucoma screening

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The document describes a method for glaucoma screening using retinal fundus images. Glaucoma is an irreversible eye disease that can cause vision loss if not detected early. The proposed method uses a novel sparse dissimilarity-constrained coding approach to segment and reconstruct the optic disc from fundus images. Reconstruction coefficients are used to calculate the cup to disc ratio, a metric for detecting glaucoma. The method was tested on 650 images and achieved better accuracy than other methods, with an average error of 0.064 compared to manual measurements. It also achieved good performance in glaucoma screening tests on two datasets. The method shows potential for large-scale population-based glaucoma screening using low-cost retinal imaging.

Logic Mind Technologies
Vijayangar (Near Maruthi Medicals), Bangalore-40
Ph: 8123668124 // 8123668066
Title: Sparse Dissimilarity-Constrained Coding for
Glaucoma Screening
Abstract—Objective: Glaucoma is an irreversible chronic eye disease that leads to vision
loss. As it can be slowed down through treatment, detecting the disease in time is
important. However, many patients are unaware of the disease because it progresses
slowly without easily noticeable symptoms. Currently, there is no effectivemethod for
low-cost population-based glaucoma detection or screening. Recent studies have shown
that automated optic nerve head assessment from 2-D retinal fundus images is promising
for low-cost glaucoma screening. In this paper, we propose a method for cup to disc ratio
(CDR) assessment using 2-D retinal fundus images. Methods: In the proposed method,
the optic disc is first segmented and reconstructed using a novel sparse
dissimilarityconstrained coding (SDC) approach which considers both the
dissimilarityconstraint and the sparsity constraint from a set of reference discs with
known CDRs. Subsequently, the reconstruction coefficients from the SDC are used to
compute the CDR for the testing disc. Results: The proposed method has been tested for
CDR assessment in a database of 650 images with CDRs manually measured by trained
professionals previously. Experimental results show an average CDR error of 0.064 and
correlation coefficient of 0.67 compared with the manual CDRs, better than the state-of-
the-art methods. Our proposed method has also been tested for glaucoma screening. The
method achieves areas under curve of 0.83 and 0.88 on datasets of 650 and 1676 images,
respectively, outperforming other methods. Conclusion: The proposed method achieves
good accuracy for glaucoma detection. Significance: The method has a great potential to
be used for large-scale population-based glaucoma screening.

Software & Hardware requirement:
1. Hardware requirement:
1. PC
2. RAM minimum 2GB
3. HDD minimum 100GB
2. Software requirement:
1. MATLAB 7.0
2. Signal processing toolbox
3. Image processing toolbox
4. Mathematical toolbox
PROJECT FLOW:
First Review:
Literature Survey
Paper Explanation
Design of Project
Project Enhancement explanation
Second Review:
Implementing 40% of Base Paper

Third Review
Implementing Remaining 60% of Base Paper with Future Enhancement
(Modification)
For More Details please contact
Logic Mind Technologies
Vijayangar (Near Maruthi Medicals), Bangalore-40
Ph: 8123668124 // 8123668066
Mail: logicmindtech@gmail.com

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This document presents a study on early detection of Alzheimer's disease using deep learning techniques. The authors build an end-to-end framework for classifying MRI images into four stages of Alzheimer's - mild demented, very mild demented, moderate demented, and non-demented. They use transfer learning with pre-trained models like VGG16 and ResNet50, achieving accuracies of 95% and 84% respectively. A custom CNN model is also developed, achieving 93% accuracy. The models are trained on a dataset of 6400 MRI images from Kaggle. A web application is also created for remote analysis and detection of Alzheimer's stage to help doctors and patients.

DIABETIC RETINOPATHY FROM FUNDUS IMAGES_VIVA VOCE_MSA.pptxmdshafeeq5

Glaucoma progressiondetection based on Retinal Features.pptxssuser097984

1. The document describes a thesis submitted to the University of Babylon for a Master's degree in Software. The thesis proposes a system for detecting glaucoma progression based on analyzing retinal features in fundus images. 2. The system includes stages for preprocessing, segmenting the optic disc and optic cup, extracting blood vessels, and extracting features for classification. Features like cup-to-disc ratio are extracted using techniques like gray-level co-occurrence matrix. 3. The contributions of the thesis include developing an automated system that can diagnose glaucoma cases and severity levels based on approved characteristics extracted from fundus images, achieving good results compared to previous work, and enabling real-time analysis of cases under 4 seconds

PSO-HRVSO: Segmentation of Retinal Vessels Through Homomorphic Filtering Enha...sipij

PSO-HRVSO: SEGMENTATION OF RETINAL VESSELS THROUGH HOMOMORPHIC FILTERING ENHA...sipij

The structure of retinal blood vessels is crucial for the early detection of diabetic retinopathy, a leading cause of blindness worldwide. Yet, accurately segmenting retinal vessels poses significant challenges due to the low contrast and noise present in capillaries.The automated segmentation of retinal blood vessels significantly enhances Computer-Aided Diagnosis for diverse ophthalmic and cardiovascular conditions. It is imperative to develop a method capable of segmenting both thin and thick retinal vessels to facilitate medical analysis and disease diagnosis effectively. This article introduces a novel methodology for robust vessel segmentation, addressing prevalent challenges identified in existing literature. The methodology PSO-HRVSO comprises three key stages: pre-processing, main processing, and postprocessing. In the initial stage, filters are employed for image smoothing and enhancement, leveraging PSO optimization. The main processing phase is bifurcated into two configurations. Initially, thick vessels are segmented utilizing an optimized top-hat approach, homo-morphic filtering, and median filter. Subsequently, the second configuration targets thin vessel segmentation, employing the optimized top-hat method, homomorphic filtering, and matched filter. Lastly, morphological image operations are conducted during the post-processing stage. The PSO-HRVSO method underwent evaluation using two publicly accessible databases (DRIVE and STARE), measuring performance across three key metrics: specificity, sensitivity, and accuracy. Analysis of the outcomes revealed averages of 0.9891, 0.8577, and 0.0.9852 for the DRIVE dataset, and 0.9868, 0.8576, and 0.9831 for the STARE dataset, respectively. The PSO-HRVSO technique yields numerical results that demonstrate competitive average values when compared to current methods. Moreover, it sur-passes all leading unsupervised methods in terms of specificity and accuracy. Additionally, it outperforms the majority of state-of-the-art supervised methods without incurring the computational costs associated with such algorithms. Detailed visual analysis reveals that the PSO-HRVSO approach enables a more precise segmentation of thin vessels compared to alternative procedures.

IRJET- Blood Vessel Segmentation in Retinal Images using MatlabIRJET Journal

This document summarizes research on blood vessel segmentation in retinal images using MATLAB. It discusses using stationary wavelet transforms and neural networks to enhance vessels and classify pixels. The research aims to implement an effective algorithm using morphological processing and segmentation techniques to detect retinal vessels and exudates. It reviews related work applying techniques like fuzzy segmentation, matched filtering, and image mining. The document concludes that analyzing retinal vessels and exudates can help detect diseases early by comparing vessel states, and the presented algorithm effectively detects retinal blood vessels.

DISCOVERING ABNORMAL PATCHES AND TRANSFORMATIONS OF DIABETICS RETINOPATHY IN ...csandit

Diabetic retinopathy (DR) is one of the retinal diseases due to long-term effect of diabetes.Early detection for diabetic retinopathy is crucial since timely treatment can prevent progressive loss of vision. The most common diagnosis technique of diabetic retinopathy is to screen abnormalities through retinal fundus images by clinicians. However, limited number of well-trained clinicians increase the possibilities of misdiagnosing. In this work, we propose a big-data-driven automatic computer-aided diagnosing (CAD) system for diabetic retinopathy severity regression based on transfer learning, which starts from a deep convolutional neural network pre-trained on generic images, and adapts it to large-scale DR datasets. From images in the training set, we also automatically segment the abnormal patches with an occlusion test,and model the transformations and deterioration process of DR. Our results can be widely used for fast diagnosis of DR, medical education and public-level healthcare propagation.

Discovering Abnormal Patches and Transformations of Diabetics Retinopathy in ...cscpconf

Diabetic retinopathy (DR) is one of the retinal diseases due to long-term effect of diabetes. Early detection for diabetic retinopathy is crucial since timely treatment can prevent progressive loss of vision. The most common diagnosis technique of diabetic retinopathy is to screen abnormalities through retinal fundus images by clinicians. However, limited number of well-trained clinicians increase the possibilities of misdiagnosing. In this work, we propose a big-data-driven automatic computer-aided diagnosing (CAD) system for diabetic retinopathy severity regression based on transfer learning, which starts from a deep convolutional neural network pre-trained on generic images, and adapts it to large-scale DR datasets. From images in the training set, we also automatically segment the abnormal patches with an occlusion test, and model the transformations and deterioration process of DR. Our results can be widely used for fast diagnosis of DR, medical education and public-level healthcare propagation.

Retinal image analysis using morphological process and clustering techniquesipij

This paper proposes a method for the Retinal image analysis through efficient detection of exudates and recognizes the retina to be normal or abnormal. The contrast image is enhanced by curvelet transform. Hence, morphology operators are applied to the enhanced image in order to find the retinal image ridges. A simple thresholding method along with opening and closing operation indicates the remained ridges belonging to vessels. The clustering method is used for effective detection of exudates of eye. Experimental result proves that the blood vessels and exudates can be effectively detected by applying this method on the retinal images. Fundus images of the retina were collected from a reputed eye clinic and 110 images were trained and tested in order to extract the exudates and blood vessels. In this system we use the Probabilistic Neural Network (PNN) for training and testing the pre-processed images. The results showed the retina is normal or abnormal thereby analyzing the retinal image efficiently. There is 98% accuracy in the detection of the exudates in the retina .

Iterative vessel segmentation of fundus imagesLogicMindtech Nologies

Early Detection of Alzheimer’s Disease Using Machine Learning TechniquesIRJET Journal

This document discusses early detection of Alzheimer's disease using machine learning techniques. It proposes using deep learning models to classify brain MRI images from three planes (coronal, axial, sagittal) to detect brain damage related to Alzheimer's with 99.5% accuracy. This high-performing model is compared to other state-of-the-art machine and deep learning models. The methodology involves training deep learning and machine learning models on MRI datasets and evaluating their performance on test data.

WOC2016POSTERMustafa Akduman

This study compared measurements of peripapillary retinal thickness from three-dimensional optical coherence tomography volume scans to traditional two-dimensional retinal nerve fiber layer thickness measurements for diagnosing primary open-angle glaucoma. The study found that certain retinal thickness parameters, such as measurements in the inferior quadrants, had diagnostic capabilities comparable or superior to retinal nerve fiber layer thickness measurements. Additionally, retinal thickness measurements exhibited a lower artifact rate compared to retinal nerve fiber layer measurements. The study concludes that assessment of peripapillary retinal thickness may provide a useful evaluation for glaucoma.