Glaucoma detection of retinal images based on boundary segmentation

Zainudin, Noraina Alia; Nazari, Ain; Mustafa, Mohd. Marzuki; Zakaria, Wan Nurshazwani Wan; Suriani, Nor Surayahani; Kairuddin, Wan Nur Hafsha Wan

doi:10.11591/ijeecs.v18.i1.pp377-384

Cited by 4 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zainudin et al [15] introduced an algorithm for the segmentation and detection of glaucoma. This algorithm described the utilization of color channel separation for pre-processing to eliminate noise for improved optic disc and optic cup segmentation.…”

Section: Introductionmentioning

confidence: 99%

Texture and pixel intensity characterization-based image segmentation with morphology and watershed techniques

Ibrahim

Al-Saleh

Jabar

2023

IJEECS

View full text Add to dashboard Cite

<p>Image segmentation is an image processing technique that concentrated on finding and locating the parts of an image such as objects and boundaries. The purpose of locating these parts is for use in further processing analysis of an image such as recognition tasks, and content-based image retrieval. This paper introduces the segmentation procedure using a proposed template of features with watershed or morphology operations. Features template based on segmentation process conveys pixels’ intensities property perceived by the threshold value of histogram representation and texture feature where the regions are characterized by their texture content using standard deviation (SD) filtering. Wiener filter and histogram equalization (HE) techniques are used as preprocessing operations to enhance the image quality. The edge detector operator is hybridized to boost the segmentation process. Some statistical metrics are used for assessing and analyzing the performance of the stages in the proposed work. As a result, this proposed template of features achieved more performance with watershed and morphology segmentation.</p>

show abstract

Section: Introductionmentioning

confidence: 99%

Texture and pixel intensity characterization-based image segmentation with morphology and watershed techniques

Ibrahim

Al-Saleh

Jabar

2023

IJEECS

View full text Add to dashboard Cite

show abstract

“…The strategy of implementing deep learning (DL) approach for the disease recognition and image distinction requires a primary training set which is annotated with those lesion symptoms. The most common technique used in the eye imaging field is the funduscopic method due to its simplicity to be handled, widely reachable, and suitability for documentation [9]- [11]. It is possible to further process those fundus pictures based on the desirable features through adjustment in spatial domain.…”

Section: Introductionmentioning

confidence: 99%

Cotton-wool spots, red-lesions and hard-exudates distinction using CNN enhancement and transfer learning

Swee

As’ari

Hitam

et al. 2021

IJEECS

View full text Add to dashboard Cite

<div>The automatic retinal disease diagnosis by artificial intelligent is an interesting and challenging topic in the medical field. It requires an appropriate image enhancement technique and a sufficient training dataset for the specific retina conditions. The aim of this study was to design an automatic diagnosis convolutional neural network (CNN) model which does not require a large training dataset to specifically identify diabetic retinopathy symptoms, which are cotton wool, exudates spots and red lesionin colour fundus pictures. A novel framework comprised image enhancement method by using upgraded contrast limited adaptive histogram equalization (UCLAHE) filter and transferred pre-trained networks was developed to classify the retinal diseases regarding to the symptoms. The performance of the proposed framework was evaluated based on accuracy, sensitivity and specificity metrics. The collected results have proven the robustness of the proposed framework in offering good accuracy in retina diseases diagnosis. </div>

show abstract