A Hybrid System for Enhancement Retinal Image Reduction

Desiani, Anita; Adrezo, Muhammad; Alfan, Anggi Miftahul; Erwin, Erwin; Suprihatin, Bambang

doi:10.1109/icimcis53775.2021.9699259

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Gamma correction is a method to change the contrast of an image by classifying the intensity of the image into bright and dark. Gamma correction is not only used to increase or decrease contrast but also to adjust the intensity of the transformation function (Desiani, Erwin, Suprihatin, Adrezo, et al, 2021). Gamma correction can produce images that are different from the original image .…”

Section: Introductionmentioning

confidence: 99%

Rotation-Gamma Correction Augmentation on CNN-Dense Block for Soil Image Classification

INDRA MAIYANTI,

DESIANI,

LAMIN

et al. 2023

Appl. Comput. Sci.

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Soil is a solid-particle that covers the earth's surface. Soils can be classified based their color. The color can be an indication of soil properties and soil conditions. Soil image classification requires high accuracy and caution. CNN works well on image classification, but CNN requires a large amount of data. Augmentation is one technique to overcome data needs like rotation and improving contrast. Rotation is the movement of rotating the image position randomly to various degrees. Gamma Correction is a method to improve image by decreasing or increasing the contrast. The rotation and Gamma Correction on augmentation can increase the amount of training data from 156 to 2500 soil images data. The classification of soil data is not referred to soil taxonomy system such as Entisols and Histosols but it used arbitrary simple classification based on color. Unfortunately, the weakness of the CNN is vanishing and exploded gradients. Another Deep learning that can overcome vanishing and exploded gradients is dense blocks. This study proposes a combination of Augmentation and CNN-Dense block where in the augmentation a combination of rotation and Gamma-correction techniques is used and Soil image classification based on color is used by the CNN-Dense block. The combination method is able to give excellent results, where all performances accuracy, precisions, recall and F1-Score are above 90%. The combination of rotation and Gamma Correction on augmentation and CNN is a robust method to use in soil image classification based on color.

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Section: Introductionmentioning

confidence: 99%

Rotation-Gamma Correction Augmentation on CNN-Dense Block for Soil Image Classification

INDRA MAIYANTI,

DESIANI,

LAMIN

et al. 2023

Appl. Comput. Sci.

Self Cite

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Denoised Non-Local Means with BDDU-Net Architecture for Robust Retinal Blood Vessel Segmentation

Desiani,

Erwin,

Suprihatin

et al. 2023

Int. J. Patt. Recogn. Artif. Intell.

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Retinal blood vessels can be obtained by image segmentation. This study proposes combining image enhancement and segmentation to obtain retinal blood vessels. The image enhancement stages use CLAHE and Denoised Non-Local Means to increase contrast and reduce noise on the original image, and Bottom-Hat (BTH) filtering is used to lighten dark features in the image so the features become lighter and darken the bright features in the image. Bottom Hat is applied to make the features of the blood vessels in the retinal image more visible. The segmentation architecture proposes BDDU-Net architecture which combines U-Net in the encoder part, DenseNet in the bridge part, and Bi-ConvLSTM in the decoder part. Image enhancement performance results are PSNR and SSIM. The PSNR is more than 40 dB on both the DRIVE and STARE datasets. The SSIM results are close to 1 on the DRIVE and STARE datasets. These results show that the image enhancement stages in the proposed method can enhance the quality of the original image. The segmentation performance results of BDDU-Net architecture are measured based on accuracy, sensitivity, specificity, IoU, and F1-Score. The DRIVE dataset obtained 95.578% for accuracy, 85.75% for sensitivity, 96.75% for specificity, 67.407% for IoU, and 80.53% for F1-Score. The STARE dataset obtained 97.63% for accuracy, 84.33% for sensitivity, 98.66% for specificity, 75.67% for IoU, and 86.15% for F1-Score. Based on the image enhancement and image segmentation results, these results show that the proposed method is great for enhancing image quality and excellent for blood vessel segmentation in retinal images, although IoU results on the DRIVE dataset need to be improved.

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A Hybrid System for Enhancement Retinal Image Reduction

Cited by 2 publications

References 15 publications

Rotation-Gamma Correction Augmentation on CNN-Dense Block for Soil Image Classification

Rotation-Gamma Correction Augmentation on CNN-Dense Block for Soil Image Classification

Denoised Non-Local Means with BDDU-Net Architecture for Robust Retinal Blood Vessel Segmentation

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