Automatic detection and recognition of optic disk with maker-controlled watershed segmentation and mathematical morphology in color retinal images

Wisaeng, Kittipol; Sa-ngiamvibool, Worawat

doi:10.1007/s00500-017-2681-9

Cited by 9 publications

(11 citation statements)

References 19 publications

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“…Therefore, it is difficult to obtain a satisfactory segmentation result by directly using the original watershed algorithm. In this study, we undertook morphological filtering and morphological gradient reconstruction 23 on the gray images before segmentation to enhance the image gray features. First, the gray images were filtered and pre-processed by morphological filtering to obtain the morphological gradient of the images.…”

Section: Instrument and Methodsmentioning

confidence: 99%

Color segmentation and extraction of yarn-dyed fabric based on a hyperspectral imaging system

Zhang

et al. 2020

Textile Research Journal

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For multi-color yarn-dyed fabrics which are cross-woven by yarns with different colors, the different colors cannot be directly measured by a traditional spectrophotometer because it can only obtain the average color of solid-color sample in the limited aperture. In this paper, a novel method for color segmentation and extraction for multi-color yarn-woven fabrics based on a Hyperspectral Imaging System (HIS) was proposed. First, the multi-color yarn-woven fabric images were acquired with the HIS. Then a space transformation based on Fréchet distance was used to transform the pre-processed hyperspectral fabric images into gray images, and then an improved watershed algorithm was used to segment the transformed gray images into different color regions. Finally, to solve the problems of over-segmentation with the improved watershed algorithm, an improved k-means clustering algorithm was adopted to merge the over-segmented color regions. The experimental results on four multi-color yarn-woven fabrics showed that the color segmentation accuracy of the proposed method outperformed the ordinary k-means, Fuzzy C-means (FCM), and Density peak cluster (DPC) algorithms on evaluation indexes of compactness (CP) and separation (SP), and the execution efficiency was improved by at least 55%. Furthermore, the color difference between the proposed method and the spectrophotometric measurements ranged from 0.60 to 0.88 CMC (2:1) (Color Measurement Committee) units, which almost satisfied the accuracy of color measurement.

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Section: Instrument and Methodsmentioning

confidence: 99%

Color segmentation and extraction of yarn-dyed fabric based on a hyperspectral imaging system

Zhang

et al. 2020

Textile Research Journal

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“…Hence many researchers have been focused over developing efficient methodologies to achieve an improved precision of OD boundary extraction. Based on the methodology accomplished at extraction, they are classified as Classifier based methods [6][7][8][9][10], Template matching based methods [11][12][13][14][15], Morphology based methods [16][17][18] and Active Contour Model based methods. Under the first class, i.e., Classifier based methods; a number of methods are developed.…”

Section: Literature Surveymentioning

confidence: 99%

“…Canny edge detection technique, assisted with further morphological operations has been used to segment the optic disk. Further, Wisaeng, Worawat [18] proposed to combine the Mathematical morphology with marker-controlled watershed segmentation [19] algorithm to detect the OD. In the class of Active Contour Model based methods, Zhou et.…”

Section: Literature Surveymentioning

confidence: 99%

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Optic Disk Segmentation through Edge Density Filter in Retinal Images

Reddy*,

Ravindran

2020

IJITEE

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Accurate and Efficient Detection of Optic Disk (OD) is an important task for the diagnosis of various eye related diseases such as Diabetic Retinopathy, Glaucoma, and papilledema in automatic retinal image analysis. This paper presents a novel and robust OD detection framework which starts with the detection of OD pixel, a center of OD region. Next, based on the ODP, a sub-image extraction is accomplished and it is processed for blood vessel removal through morphological technique. Finally, a new filter, called as Edge Density Filter (EDF) is accomplished to find out the extract the OD region in the Blood vessel removed, Edge detected and binarized sub-image. Two publicly available datasets, MESSIDOR and DRIVE were used to evaluate the performance of proposed method and the performance is measured through performance metrics such as Overlap Score, Sensitivity and Accuracy.

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“…After testing these different idea on DIARETDB1 database, the combination of the intensity information and largest connected component leads to a good solution for the detection of OD. This proposed methods including mathematical morphology, OTSU thresholding, maker-controlled watershed segmentation and Sobel edge operator, which proposed by Wisaeng and Sa-ngiamvibool [32]. Firstly, the retinal structures, including bright region, vessels and OD, which can mislead exudates candidate detection, a morphological by closing operator is used on the pre-processed image to remove retinal structure element.…”

Section: A Preprocessingmentioning

confidence: 99%

Exudates Detection Using Morphology Mean Shift Algorithm in Retinal Images

Wisaeng

Sa-ngiamvibool

2019

IEEE Access

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Exudates are a serious complication causing blindness in diabetic retinopathy patients. The main objective of this paper is to develop a novel method to detect exudates lesions in color retinal images by using a morphology mean shift algorithm. The proposed method start with a normalization of the retinal image, contrast enhancement, noise removal, and the localization of the OD. Then, a coarse segmentation method by using mean shift provides a set of exudates and non-exudates candidates. Finally, a classification using the mathematical morphology algorithm (MMA) procedure is applied in order to keep only exudates pixels. The optimal value parameters of the MMA will facilitate an increase of the accuracy results from the solely MSA method by 13.10%. Based on a comparison between the results and ground truth images, the proposed method obtained an average sensitivity, specificity, and accuracy of detecting exudates as 98.40%, 98.13%, and 98.35%, respectively.

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Automatic detection and recognition of optic disk with maker-controlled watershed segmentation and mathematical morphology in color retinal images

Cited by 9 publications

References 19 publications

Color segmentation and extraction of yarn-dyed fabric based on a hyperspectral imaging system

Color segmentation and extraction of yarn-dyed fabric based on a hyperspectral imaging system

Optic Disk Segmentation through Edge Density Filter in Retinal Images

Exudates Detection Using Morphology Mean Shift Algorithm in Retinal Images

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