Marker controlled watershed transform for intra-retinal cysts segmentation from optical coherence tomography B-scans (original) (raw)
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Segmentation of Retinal Cysts From Optical Coherence Tomography Volumes Via Selective Enhancement
IEEE Journal of Biomedical and Health Informatics
Automated and accurate segmentation of cystoid structures in Optical Coherence Tomography (OCT) is of interest in the early detection of retinal diseases. It is however a challenging task. We propose a novel method for localizing cysts in 3D OCT volumes. The proposed work is biologically inspired and based on selective enhancement of the cysts, by inducing motion to a given OCT slice. A Convolutional Neural Network (CNN) is designed to learn a mapping function that combines the result of multiple such motions to produce a probability map for cyst locations in a given slice. The final segmentation of cysts is obtained via simple clustering of the detected cyst locations. The proposed method is evaluated on two public datasets and one private dataset. The public datasets include the one released for the OPTIMA Cyst segmentation challenge (OCSC) in MICCAI 2015 and the DME dataset. After training on the OCSC train set, the method achieves a mean Dice Coefficient (DC) of 0.71 on the OCSC test set. The robustness of the algorithm was examined by cross validation on the DME and AEI (private) datasets and a mean DC values obtained were 0.69 and 0.79, respectively. Overall, the proposed system outperforms all benchmarks. These results underscore the strengths of the proposed method in handling variations in both data acquisition protocols and scanners.
Automatic cyst detection in OCT retinal images combining region flooding and texture analysis
2013
In this work Optical Coherence Tomography (OCT) retinal images are automatically processed to detect the presence of cysts. The methodology is composed by three phases: region of interest where cysts will be searched is delimited; a watershed algorithm is applied to find all the possible regions in the image which might conform cystic structures; finally, texture analysis is performed in each region from previous phase to final classification. Results show that accuracy achieved with this method is over 80%.
Journal of Medical Signals & Sensors, 2016
images suffer from speckle noise which causes difficulty in the actual detection of retinal layers [Figure 1] [6] and the shape of structural features such as drusens, macular holes, macular edema, nerve fiber atrophy, and cysts, that can be used as markers in clinical investigation and diagnostics of retinal diseases. Hence, before segmentation of this region in OCT images, the development of algorithmic approaches to provide noise suppression must be performed. In recent years, some approaches have been heavily investigated for speckle noise reduction, such as anisotropic diffusion-based methods, [7-9] wavelet-based methods, [10] dual-tree complex wavelet transformation, [11] curvelet transform, [12] contourlet transform, [13] sparsity-based denoising, [14,15] dictionary learning-based methods. [16,17] www.jmss.mui.ac.ir
Localization of Optic Disc in Retinal Images by Using an Efficient K-Means Clustering Algorithm
2014
In this paper, we proposed algorithm for localization of optic disc as it aids to detect different phases of diabetic retinopathy. The proposed algorithm gives accurate results for localization of optic disc. There are three stages defined in this paper for localization of optic disc. First stage is pre-processing in which the retinal image is enhanced. Second stage is clustering in which K-means algorithm is applied. Third stage is post-processing where morphological operations are performed. The proposed algorithm is applied on the DRIVE dataset. The results obtain when the number of clusters chosen are 16 and 20. Keywords—Optic Disc, Adaptive Histogram Equalization, K-means algorithm, Mathematical Morphology
Computational Intelligence Methods for …, 2011
In this paper an automatic unsupervised method for the segmentation of retinal vessels is proposed. A Self-Organizing Map is trained on a portion of the same image that is tested and K-means clustering algorithm is used to divide the map units in 2 classes. The entire image is again input for the Self-Organizing Map, and the class of each pixel will be the class of the best matching unit on the Self-Organizing Map. Finally, the vessel network is post-processed using a hill climbing strategy on the connected components of the segmented image.
Optical Coherence Tomography (OCT) in ophthalmology is an emerging technology that can provide high-resolution crosssectional images of the retina for identifying, and quantitatively assessing of the disease. In previous researches, we proposed some automatic measurement methods of the thickness between Inner Limiting Membrane (ILM) and Retinal Pigment Epithelium (RPE) from OCT images. The method has a large problem that cannot extract the border of an abnormal part for the evaluation of the effect in some cases using the treatment by medical doctors. In this paper, we propose a new method to measure the disease area using regional statistics, that is, mean and standard deviation. The objective of this research is to extract the border of abnormal part so that we can evaluate the effectiveness of the treatment by comparing the abnormal area before and after taking treatment or between the previous observed images and a new current image. The method calculates the mean and standard deviation of gray level in the region of interest (ROI) in the abnormal area at pointed area by medical doctor. These values compare with every ROI in the abnormal area to determine, extract, and calculate the disease area. The accuracy of the proposed automatic extraction is more than 90%.
A Simple Method for Optic Disk Segmentation from Retinal Fundus Image
—Detection of optic disc area is complex because it is located in an area that is considered as pathological blood vessels when in segmentation and thus require a method to detect the area of the optic disc, this paper proposed the optic disc segmentation using a method that has not been used before, and this method is very simple, K-means clustering is a proposed Method in this paper to detect the optic disc area with perfected using adaptive morphology. This paper successfully detect optic disc area quickly and segmented blood vessels more quickly.
Automatic Segmentation of Retinal Nerves by Improved Fuzzy-C-Means Clustering
International Journal of Applied Information Systems, 2015
Computer Aided Detection of medical image has been an improved step in the early diagnosis of diseases present in the body. Developing an efficient algorithm for medical image segmentation has been a demanding area of growing research of interest during the last decades. The initial step in computer aided diagnosis of retinal medical image is generally to segment the nerves present in it. The second step is to analyze each area separately to find the presence of pathologies in it. This paper reports on segmenting of the nerves by separating the retinal images using the combination of Improved Fuzzy-C-Means Clustering along with the Enhanced multidimensional multiscale parser (EMMP) algorithm. The performance of this proposed approach is proved to be better for a threshold value of 120. From the experimental results, it has been observed that the proposed segmentation approach provides better segmentation accuracy of 97.4 % in segmenting Retinal nerves.
Physica Medica, 2018
Iris neoplasm is a non-symptom cancer that causes a gradual loss of sight. The first purpose of this study was to present a novel and automatic method for segmenting the iris tumors and detecting the corresponding areas changing along time. The second aim of this work was to investigate several recently published methods after being applied for the iris tumors segmentation. Methods: Our approach consists firstly in segmenting the iris region by using the Vander Lugt correlator based active contour method. Secondly, by treating only the iris region, a K-means clustering model was used to assign the tumorous tissue to one pixel-cluster. This model is quite sensitive to the center initialization and to the choice of the distance measure. To solve these problems, a proportional probability based approach was introduced for the cluster center initialization, and the impact of several distance measure was investigated. The proposed method and the different comparative methods were evaluated on two databases: the Eye Cancer and the Miles Research. Results: Results reported using several performance metrics reveal that the first step assures the detection of all iris tumors with an accuracy of 100%. Additionally, the proposed method yields better performance compared to the recently published methods.
Retinal Layer Segmentation in Optical Coherence Tomography Images
The four major causes of blindness are age-related diseases, out of which three affects the retina. These diseases, i.e., glaucoma, diabetic retinopathy, and age-related macular degeneration, require lifelong treatment and cause irreversible blindness. Conversely, early diagnosis has been shown to curtail or prevent blindness and visual impairments. A critical element of the clinical diagnosis is the analysis of individual retinal layer properties, as the manifestation of the dominant eye diseases has been shown to correlate with structural changes to the retinal layers. Regrettably, manual segmentation is dependent on the ophthalmologist's level of expertise, and currently becoming impractical due to advancement in imaging modalities. Inherently, much research on computer-aided diagnostic methods is conducted to aid in extracting useful layer information from these images, which were inaccessible without these techniques. However, speckle noise and intensity inhomogeneity remain a challenge with a detrimental effect on the performance of automated methods. In this paper, we propose a method comprising of fuzzy image processing techniques and graph-cut methods to robustly segment optical coherence tomography (OCT) into five (5) distinct layers. Notably, the method establishes a specific region of interest to suppress the interference of speckle noise, while Fuzzy C-means is utilized to build data terms for better integration into the continuous max-flow to handle inhomogeneity. The method is evaluated on 225 OCT B-scan images, and promising experimental results were achieved. The method will allow for early diagnosis of major eye diseases by providing the basic, yet critical layer information necessary for an effective eye examination. INDEX TERMS Medical image analysis, optical coherence tomography, fuzzy image processing, graph-cut, continuous max-flow.