Adaptive weighted locality-constrained sparse coding for glaucoma diagnosis

Zhou, Wei; Yi, Yugen; Bao, Jining; Wang, Wenle

doi:10.1007/s11517-019-02011-z

Cited by 13 publications

(18 citation statements)

References 46 publications

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“…For another, the performance of the proposed approach is compared against the state-of-the-art approaches, i.e., wavelet features [52], superpixel segmentation [53], semisupervised clustering [54], deep learning [55], and AWLCSC [31]. Table 5 shows the diagnosis results obtained by different algorithms on the REFUGE database and RIM-ONE r2 database, respectively.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…In feature extraction stage, a series of important and distinctive hand-crafted features will be extracted to explore the concealed pixel variations in the retinal images. The extracted hand-crafted features are classified into wavelet decomposition-based features [29], morphological-based features [30], nonlinear-based features [31], textural-based features [32], and image descriptorbased features [33]. After feature extraction, the last process is classification.…”

Section: Machine Learning-(ml-) Basedmentioning

confidence: 99%

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Unsupervised Anomaly Detection for Glaucoma Diagnosis

Zhou

Yuan²,

et al. 2021

Wireless Communications and Mobile Computing

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With the rapid development of high tech, Internet of Things (IoT) and artificial intelligence (AI) achieve a series of achievements in the healthcare industry. Among them, automatic glaucoma diagnosis is one of them. Glaucoma is second leading cause of blindness in the world. Although many automatic glaucoma diagnosis approaches have been proposed, they still face the following two challenges. First, the data acquisition of diseased images is extremely expensive, especially for disease with low occurrence, leading to the class imbalance. Second, large-scale labeled data are hard to obtain in medical image domain. The aforementioned challenges limit the practical application of these approaches in glaucoma diagnosis. To address these disadvantages, this paper proposes an unsupervised anomaly detection framework based on sparse principal component analysis (SPCA) for glaucoma diagnosis. In the proposed approach, we just employ the one-class normal (nonglaucoma) images for training, so the class imbalance problem can be avoided. Then, to distinguish the glaucoma (abnormal) images from the normal images, a feature set consisting of segmentation-based features and image-based features is extracted, which can capture the shape and textural changes. Next, SPCA is adopted to select the effective features from the feature set. Finally, with the usage of the extracted effective features, glaucoma diagnosis can be automatically accomplished via introducing the T 2 statistic and the control limit, overcoming the issue of insufficient labeled samples. Extensive experiments are carried out on the two public databases, and the experimental results verify the effectiveness of the proposed approach.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Machine Learning-(ml-) Basedmentioning

confidence: 99%

Unsupervised Anomaly Detection for Glaucoma Diagnosis

Zhou

Yuan²,

et al. 2021

Wireless Communications and Mobile Computing

Self Cite

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show abstract

“…After the optic disc and optic cup boundaries extraction, we use the accuracy as a common measurement for glaucoma assessment. In this section, some state-of-the-art glaucoma diagnosis approaches are employed for verifying the effectiveness of the proposed approach, i.e., superpixel segmentation [3], wavelet features [44], multifeature fusion [45], deep learning [46], SDC [47], and AWLCSC [48]. Table 5 shows the classification results obtained by different algorithms on the DRISHTI-GS and RIM-ONE r2 databases.…”

Section: Resultsmentioning

confidence: 99%

Optic Disc and Cup Segmentation in Retinal Images for Glaucoma Diagnosis by Locally Statistical Active Contour Model with Structure Prior

Zhou

Yuan

et al. 2019

Computational and Mathematical Methods in Medicine

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Accurate optic disc and optic cup segmentation plays an important role for diagnosing glaucoma. However, most existing segmentation approaches suffer from the following limitations. On the one hand, image devices or illumination variations always lead to intensity inhomogeneity in the fundus image. On the other hand, the spatial prior knowledge of optic disc and optic cup, e.g., the optic cup is always contained inside the optic disc region, is ignored. Therefore, the effectiveness of segmentation approaches is greatly reduced. Different from most previous approaches, we present a novel locally statistical active contour model with the structure prior (LSACM-SP) approach to jointly and robustly segment the optic disc and optic cup structures. First, some preprocessing techniques are used to automatically extract initial contour of object. Then, we introduce the locally statistical active contour model (LSACM) to optic disc and optic cup segmentation in the presence of intensity inhomogeneity. Finally, taking the specific morphology of optic disc and optic cup into consideration, a novel structure prior is proposed to guide the model to generate accurate segmentation results. Experimental results demonstrate the advantage and superiority of our approach on two publicly available databases, i.e., DRISHTI-GS and RIM-ONE r2, by comparing with some well-known algorithms.

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“…RIM-One-r2, published in 2014, is an extension of the first version having some duplicated images, containing 255 photographs of healthy eyes and 200 photos of patients with glaucoma at HUC and Hospital Universitário Miguel Servet using the same cameras as the previous version. Furthermore, medical experts manually segmented images [56,58].…”

Section: Public Databasesmentioning

confidence: 99%

Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification

et al. 2022

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Artificial intelligence techniques are now being applied in different medical solutions ranging from disease screening to activity recognition and computer-aided diagnosis. The combination of computer science methods and medical knowledge facilitates and improves the accuracy of the different processes and tools. Inspired by these advances, this paper performs a literature review focused on state-of-the-art glaucoma screening, segmentation, and classification based on images of the papilla and excavation using deep learning techniques. These techniques have been shown to have high sensitivity and specificity in glaucoma screening based on papilla and excavation images. The automatic segmentation of the contours of the optic disc and the excavation then allows the identification and assessment of the glaucomatous disease’s progression. As a result, we verified whether deep learning techniques may be helpful in performing accurate and low-cost measurements related to glaucoma, which may promote patient empowerment and help medical doctors better monitor patients.

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Adaptive weighted locality-constrained sparse coding for glaucoma diagnosis

Cited by 13 publications

References 46 publications

Unsupervised Anomaly Detection for Glaucoma Diagnosis

Unsupervised Anomaly Detection for Glaucoma Diagnosis

Optic Disc and Cup Segmentation in Retinal Images for Glaucoma Diagnosis by Locally Statistical Active Contour Model with Structure Prior

Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification

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