Optic cup segmentation using adaptive threshold and morphological image processing

Nugroho, Hanung Adi; Kirana, Thea; Pranowo, Vicko; Hutami, Augustine Herini Tita

doi:10.21924/cst.4.2.2019.125

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Morphological operation algorithms can be developed and differentiated based on the sequence of these two processes. An opening algorithm is when erosion is performed first, and dilation is performed subsequently [35]. When the sequence is otherwise, the algorithm is called a closing algorithm.…”

Section: Morphological Operationsmentioning

confidence: 99%

A new approach for sensitivity improvement of retinal blood vessel segmentation in high-resolution fundus images based on phase stretch transform

Firdausy

Wahyunggoro²,

Nugroho³

et al. 2022

Int. J. Adv. Intell. Informatics

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The eye-fundus photograph is widely used for eye examinations. Accurate identification of retinal blood vessels could reveal information that is helpful for clinical diagnoses of many health disorders. Although several methods have been proposed to segment images of retinal blood vessels, the sensitivity of these methods is plausible to be improved. The algorithm’s sensitivity refers to the algorithm’s ability to identify retinal vessel pixels correctly. Furthermore, the resolution and quality of retinal images are improving rapidly. Consequently, new segmentation methods are in demand to overcome issues from high-resolution images. This study presented improved performance of retinal vessel segmentation using a novel edge detection scheme based on the phase stretch transform (PST) function as its kernel. Before applying the edge detection stage, the input retinal images were pre-processed. During the pre-processing step, non-local means filtering on the green channel image, followed by contrast limited adaptive histogram equalization (CLAHE) and median filtering, were applied to enhance the retinal image. After applying the edge detection stage, the post-processing steps, including the CLAHE, median filtering, thresholding, morphological opening, and closing, were implemented to obtain the segmented image. The proposed method was evaluated using images from the high-resolution fundus (HRF) public database and yielded promising results for sensitivity improvement of retinal blood vessel detection. The proposed approach contributes to a better segmentation performance with an average sensitivity of 0.813, representing a clear improvement over several benchmark techniques

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Section: Morphological Operationsmentioning

confidence: 99%

A new approach for sensitivity improvement of retinal blood vessel segmentation in high-resolution fundus images based on phase stretch transform

Firdausy

Wahyunggoro²,

Nugroho³

et al. 2022

Int. J. Adv. Intell. Informatics

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show abstract

“…Various methods have been utilized to aid in the early detection of breast cancer, such as mammography, ultrasound, and magnetic resonance imaging (MRI). Thermographic techniques are currently being studied to show promising results [3][4][5][6]. It can capture the temperature difference generated by the heat of the tumor area in the body, where the area is hotter than the healthy area [3].…”

Section: Introductionmentioning

confidence: 99%

A systematic review of breast cancer detection on thermal images

Aqil Aqthobirrobbany,

Dian Nova Kusuma Hardani,

Indah Soesanti

et al. 2023

CST

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Breast cancer poses a substantial global health concern, primarily regarding its impact on women. Thermal imaging has emerged as a promising tool for early detection with notable technological advancements between 2013 and 2023 in enhancing diagnostic capabilities. However, existing literature reviews often lack adherence to specific scholarly standards and may provide incomplete insights into research trends. This systematic literature review (SLR) addresses these issues by comprehensively analyzing research trends, publication types, contributions, datasets, methodologies, and effective approaches for breast cancer detection using thermal imaging. The review encompasses an examination of 40 articles from reputable digital libraries, revealing a predominant emphasis on deep learning algorithms among 25 applied methods. These algorithms consistently achieve commendable performance, frequently surpassing 90% accuracy rates. Consequently, current research in breast cancer detection via thermal imaging is marked by a strong focus on artificial intelligence, particularly machine and deep learning, recognized as the most promising and effective avenues for investigation.

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Nondestructive Evaluation of the Isolated Pore Spatial Distribution State in a Special-Shaped C/SiC Composite Component

Qu,

Wen,

et al. 2023

J Nondestruct Eval

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Optic cup segmentation using adaptive threshold and morphological image processing

Cited by 4 publications

References 12 publications

A new approach for sensitivity improvement of retinal blood vessel segmentation in high-resolution fundus images based on phase stretch transform

A new approach for sensitivity improvement of retinal blood vessel segmentation in high-resolution fundus images based on phase stretch transform

A systematic review of breast cancer detection on thermal images

Nondestructive Evaluation of the Isolated Pore Spatial Distribution State in a Special-Shaped C/SiC Composite Component

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