Automatic Optic Disc Detection in Retinal Images Using FKMT‒MOPDF

Wisaeng, Kittipol

doi:10.32604/csse.2023.033226

Cited by 3 publications

(2 citation statements)

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“…Wisaeng In 2023 [30], this study, proposes an approach combining Fuzzy K-Means Thresholding (FKMT) and Morphological Operations with Pixel Density Features (MOPDF) to address these challenges. Initially, the input retinal images are threshold and fuzzy K-Means clustering is used to coarsely segment them, distinguishing the OD from neighbouring organs with similar intensities.…”

Section: Maiti Et Al In 2022mentioning

confidence: 99%

The Optic Disc Detection and Segmentation in Retinal Fundus Images Utilizing You Only Look Once (YOLO) Method

Jabbar Hussein,

Hamood Al-Saadi

2024

IJCDS

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Automated analysis of retinal images is a crucial diagnostic method for the early detection of disorders affecting the eyes, such as glaucoma, diabetic macula edema (DME), and retinopathy brought on by diabetes. The current study introduces a reliable technique for segmenting and detecting optic discs using a deep learning-based technique. This is comparable to the initial stage of creating a diagnostic system supported by a computer for diabetic macula edema in retinal images. The suggested approach is predicated on the recommended approach using the YOLO (You Only Look Once) algorithm for detection objects and segmentation for bounding boxes that belong to the same category, comparing the Intersection over Union (IOU) values of each bounding box with those of the others. If IOU values are higher than thresholds, they will consider them the same targets and maintain the boundary boxes with the highest reliability. Three retinal image databases that are accessible to the public are used to quantitatively assess the technique: Messidor-1, Messidor-2, and the IDRID Database. The technique yields a success rate of 99.5% for optic disc identification, a precision of 99.9%, and a recall of 100% for Messidor-1, and testing for all Messidor-2 and IDRID databases accepts accuracy of 99.1% and 98.7% respectively. When it comes to the identification and border extraction of the optic disc, this special technique has demonstrated a notable improvement over the previous approach.

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Section: Maiti Et Al In 2022mentioning

confidence: 99%

The Optic Disc Detection and Segmentation in Retinal Fundus Images Utilizing You Only Look Once (YOLO) Method

Jabbar Hussein,

Hamood Al-Saadi

2024

IJCDS

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“…In the case of the MRI dataset, which comprises 253 images, 98 are normal, and 155 are brain tumor images, all of which have undergone a thorough review by three medical experts. The Dice similarity index is calculated as two times the region of the intersection of A and B, divided by the sum of the region of A and B: Dice=2 |A∩B|/(|A|+|B|) =2TP/(2TP+FP+FN) (TP=True Positives, FP=False Positives, and FN=False Negatives) [31], [32]. Note: The count of brain tumor pixels with an intensity of 1 in image A is the number of positives, while the total number of brain tumor pixels with a value of 1 in both A and B is referred to as the number of true positives or TP.…”

Section: ) Dice Similarity Indexmentioning

confidence: 99%

U-Net++DSM: Improved U-Net++ for Brain Tumor Segmentation With Deep Supervision Mechanism

Wisaeng

2023

IEEE Access

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The segmentation of brain tumors is an important and challenging content in medical image processing. Relying solely on human experts to manually segment large volumes of data can be timeconsuming and delay diagnosis. To address this challenge, researchers have set out to develop an algorithm that can automatically determine whether MRI images contain brain tumors and identify their features. This paper proposes the U-Net++DSM, a collaborative approach combining U-Net++ with Deep Supervision Mechanism (DSM) as its backbone. To enhance the segmentation power of U-Net++DSM, medical professionals have trained a dilation operator using fully annotated images. The results of this method demonstrate that the combination of U-Net++DSM and the dilation operator significantly improves segmentation accuracy, especially when the number of fully-labeled images is limited. The results show that the proposed U-Net++DSM outperforms traditional U-Net models by achieving high segmentation performance, surpassing other state-of-the-art models, with a sensitivity of 98.59.00%, a specificity of 98.72%, an accuracy of 98.64%, and an average Dice score of 98.81% when tested on publicly available databases. Compared to other existing segmentation methods, the U-Net++DSM method has the potential to yield even better brain tumor segmentation results in terms of pixel-based classification and dice similarity performance metrics.

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Simultaneous Detection of the Optic Disc and Exudates in Color Retinal Images

Wisaeng,

Eampoonga

2024

2024 5th Technology Innovation Management and Engineering Science International Conference (TIMES-iCON)

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Automatic Optic Disc Detection in Retinal Images Using FKMT‒MOPDF

Cited by 3 publications

References 37 publications

The Optic Disc Detection and Segmentation in Retinal Fundus Images Utilizing You Only Look Once (YOLO) Method

The Optic Disc Detection and Segmentation in Retinal Fundus Images Utilizing You Only Look Once (YOLO) Method

U-Net++DSM: Improved U-Net++ for Brain Tumor Segmentation With Deep Supervision Mechanism

Simultaneous Detection of the Optic Disc and Exudates in Color Retinal Images

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