Automatic imaging method for optic disc segmentation using morphological techniques and active contour fitting

Agarwal, Ashi; Issac, Ashish; Singh, Anushikha; Dutta, Malay Kishore

doi:10.1109/ic3.2016.7880227

Cited by 11 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table III and Table IV discussed performance of the proposed GlaucoNet+ model with different classification setups (Table III) and configuration (Table IV); however to further examine efficacy of our proposed model in comparison to the other existing glaucoma detection and classification methods, we have performed analysis based on secondary resources (reviewing existing methods or allied papers). [49] 84.38 --- [54] 95.50 --- [60] --- [64] 88.00 --- [35] --98.60 - [36] 99.20 -86.00 - [65] ---- [47] 80.00 -95.00 - [48] 97.00 --- [57] 98 --- [39] 94.10 -91.80 - [75] 90.00 --- [40] --92.00 - [33] 100.00 -94.00 - [59] ---- [66] 89.6 (NB) 97.6(AN N) --- [67] 92.00 --- [68] ---- [69] 72.38 --- [70] 79.00 -87.00 - [71] 83.10 --- [62] 93.00 --- [72] 96.67 -100.00 - [73] 91.00 --- [74] 92.00 --- [4] 0.8478 - [55] 88. Observing the results, it can be found that the proposed GlaucoNet+ model with Hybrid feature extraction and SVM (polynomial) with 10-fold cross validation outperforms major existing approaches.…”

Section: Resultsmentioning

confidence: 99%

Hybrid CNN assisted Computer Aided Diagnosis System for Glaucoma Detection and Classification: GlaucoNet+

2019

IJITEE

View full text Add to dashboard Cite

The exponential rise in technologies has revitalized academia-industries to achieve more efficient computer aided diagnosis systems. It becomes inevitable especially for Glaucoma detection which has been increasing with vast pace globally. Most of the existing approaches employs morphological features like optical disk and optical cup information, optical cup to disk ratio etc; however enabling optimal detection of such traits has always been challenge for researchers. On the other hand, in the last few years deep learning methods have gained widespread attention due to its ability to exploit fine grained features of images to make optimal classification decision. However, reliance of such methods predominantly depends on the presence of deep features demanding suitable feature extraction method. To achieve it major existing approaches extracts full-image features that with high dimensional kernel generates gigantically huge features, making classification computationally overburdened. Therefore, retaining optimal balance between deep features and computational overhead is of utmost significance for glaucoma detection and classification. With this motive, in this paper a novel hybrid deep learning model has been developed for Glaucoma detection and classification. The proposed Hybrid CNN model embodies Stacked Auto-Encoder (SAE) with transferable learning model AlexNet that extracts high dimensional features to make further two-class classification. To achieve computational efficiency, In addition to the classical ReLu and dropout (50%), we used Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) algorithms. We applied 10-fold cross validation assisted Support Vector Machine classifier to perform two-class classification; Glaucomatous and Normal fundus images. Simulation results affirmed that the proposed Hybrid deep learning model with LDA feature selection and SVM-Poly classification achieves the maximum accuracy of 98.8%, precision 97.5%, recall 97.5% and F-Measure of 97.8%.

show abstract

Section: Resultsmentioning

confidence: 99%

Hybrid CNN assisted Computer Aided Diagnosis System for Glaucoma Detection and Classification: GlaucoNet+

2019

IJITEE

View full text Add to dashboard Cite

show abstract

“…Bock et al [6] calculated Glaucoma Risk Index (GRI) from the pre-processed image with the use of the probabilistic two-stage scheme. Agarwal et al [4] implemented classic morphological operations iteratively followed by active contour fitting for the precise segmentation of the optic disc. Issac et al [26] utilized the concept of super pixels for the removal of false positives followed by the analysis of geometrical features to accurately segment out the optic disc.…”

Section: Related Workmentioning

confidence: 99%

Improved optic disc and cup segmentation in Glaucomatic images using deep learning architecture

Mangipudi

Pandey

Choudhary

2021

Multimed Tools Appl

View full text Add to dashboard Cite

Glaucoma is an ailment causing permanent vision loss but can be prevented through the early detection. Optic disc to cup ratio is one of the key factors for glaucoma diagnosis. But accurate segmentation of disc and cup is still a challenge. To mitigate this challenge, an effective system for optic disc and cup segmentation using deep learning architecture is presented in this paper. Modified Groundtruth is utilized to train the proposed model. It works as fused segmentation marking by multiple experts that helps in improving the performance of the system. Extensive computer simulations are conducted to test the efficiency of the proposed system. For the implementation three standard benchmark datasets such as DRISHTI-GS, DRIONS-DB and RIM-ONE v3 are used. The performance of the proposed system is validated against the state-of-the-art methods. Results indicate an average overlapping score of 96.62%, 96.15% and 98.42% respectively for optic disc segmentation and an average overlapping score of 94.41% is achieved on DRISHTI-GS which is significant for optic cup segmentation.

show abstract

“…The optic disc is segmented as an elevated structure possessing the fine retinal vessels and nerve endings [34]. Contour of each and every fine structure in the image is described through geometric or geodesic active contours [7,29]. This property of the active contour model can be used more effectively in 3-D image construction and mapping.…”

Section: Geometric or Geodesic Active Contour Modelsmentioning

confidence: 99%

Active Contour Based Segmentation Techniques for Medical Image Analysis

Hemalatha¹,

Thamizhvani²,

Dhivya³

et al. 2018

Medical and Biological Image Analysis

View full text Add to dashboard Cite

Image processing is a technique which is used to derive information from the images. Segmentation is a section of image processing for the separation or segregation of information from the required target region of the image. There are different techniques used for segmentation of pixels of interest from the image. Active contour is one of the active models in segmentation techniques, which makes use of the energy constraints and forces in the image for separation of region of interest. Active contour defines a separate boundary or curvature for the regions of target object for segmentation. The contour depends on various constraints based on which they are classified into different types such as gradient vector flow, balloon and geometric models. Active contour models are used in various image processing applications specifically in medical image processing. In medical imaging, active contours are used in segmentation of regions from different medical images such as brain CT images, MRI images of different organs, cardiac images and different images of regions in the human body. Active contours can also be used in motion tracking and stereo tracking. Thus, the active contour segmentation is used for the separation of pixels of interest for different image processing.

show abstract

Automatic imaging method for optic disc segmentation using morphological techniques and active contour fitting

Cited by 11 publications

References 10 publications

Hybrid CNN assisted Computer Aided Diagnosis System for Glaucoma Detection and Classification: GlaucoNet+

Hybrid CNN assisted Computer Aided Diagnosis System for Glaucoma Detection and Classification: GlaucoNet+

Improved optic disc and cup segmentation in Glaucomatic images using deep learning architecture

Active Contour Based Segmentation Techniques for Medical Image Analysis

Contact Info

Product

Resources

About