Segmentasi Citra Menggunakan Metode Watershed Transform Berdasarkan Image Enhancement Dalam Mendeteksi Embrio Telur

Saifullah, Shoffan

doi:10.29080/systemic.v5i2.798

Cited by 12 publications

(13 citation statements)

References 22 publications

(23 reference statements)

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“…Based on this process, the digital image was created and processed by other image preprocessing methods. Image preprocessing in this study is image grayscaling, image adjustment, and image enhancement [20], [30].…”

Section: Methodsmentioning

confidence: 99%

“…Image enhancement uses two combined methods of Histogram Equalization (HE) and its derivative Contrast Limited Adaptive Histogram Equalization (CLAHE). The use of this method is based on the previous research, that the resulting image shows better results than single methods or other combinations [29], [30], [33]. This process uses the concept of image histogram modification.…”

Section: Methodsmentioning

confidence: 99%

“…Then the following process is saturated detection to clarify the detected embryo objects. It is what distinguishes it from previous studies using methods such as k-means and morphology [16], [17], thresholding [3], otsu [18], Fuzzy logic [19], watershed [20], region generation [21], Differential Interference Contrast (DIC) based Shape Index and Ellipsoid-Fitting [22].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detection of Chicken Egg Embryos using BW Image Segmentation and Edge Detection Methods

Saifullah

Suryotomo

Yuhefizar

2021

RESTI

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This study aims to identify chicken egg embryos with the concept of image processing. This concept uses input and output in images. Thus the identification process, which was originally carried out using manual observation, was developed by computerization. Digital images are applied in identification by various image preprocessing, image segmentation, and edge detection methods. Based on these three methods, image processing has three processes: image grayscaling (convert to a grayscale image), image adjustment, and image enhancement. Image adjustment aims to clarify the image based on color correction. Meanwhile, image enhancement improves image quality, using histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization methods (CLAHE). Specifically for the image enhancement method, the CLAHE-HE combination is used for the improvement process. At the end of the process, the method used is edge detection. In this method, there is a comparison of various edge detection operators such as Roberts, Prewitt, Sobel, and canny. The results of edge detection using these four methods have the SSIM value respectively 0.9403; 0.9392; 0.9394; 0.9402. These results indicate that the SSIM values of the four operators have the same or nearly the same value. Thus, the edge detection method can provide good edge detection results and be implemented because the SSIM value is close to 1.00 (more than 0.93). Image segmentation detected object (egg and embryo), and the continued process by edge detection showed clearly edge of egg and embryo.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Chicken Egg Embryos using BW Image Segmentation and Edge Detection Methods

Saifullah

Suryotomo

Yuhefizar

2021

RESTI

Self Cite

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“…Repairs were carried out using a combination of the HE and CLAHE methods. This combination can provide a clearer and easier image for the next process [3]. The histogram repair is carried out based on the histogram shown in Figure 5, whose distribution follows the original image.…”

Section: Preprocessing Phase IImentioning

confidence: 99%

Fish detection using morphological approach based-on k-means segmentation

2021

Self Cite

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Image segmentation is a concept that is often used for object detection. This detection has difficulty detecting objects with backgrounds that have many colors and even have a color similar to the object being detected. This study aims to detect fish using segmentation, namely segmenting fish images using k-means clustering. The segmentation process is processed by improving the image first. The initial process is preprocessing to improve the image. Preprocessing is done twice, before segmentation using k-means and after. Preprocessing stage 1 using resize and reshape. Whereas after k-means is the contrast-limited adaptive histogram equalization. Preprocessing results are segmented using k-means clustering. The K-means concept classifies images using segments between the object and the background (using k = 8). The final step is the morphological process with open and close operations to obtain fish contours using black and white images based on grayscale images from color images. Based on the experimental results, the process can run well, with the ssim value close to 1, which means that image information does not change. Processed objects provide a clear picture of fish objects so that this k-means segmentation can help detect fish objects.

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“…The resizing method aims to reduce the image and speed up processing. The image enhancement uses the concept of histogram equalization and its derivatives (i.e., CLAHE and HE [19], [20]).…”

Section: Introductionmentioning

confidence: 99%

Chicken Egg Fertility Identification using FOS and BP-Neural Networks on Image Processing

Saifullah

Suryotomo

2021

RESTI

Self Cite

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This article aims to test FOS (first-order statistical) in extracting features of embryonated eggs. This test uses the initial step of image processing to get the best input image in feature extraction. The image processing method starts from the image acquisition process, then improves with image preprocessing and segmentation. Image acquisition in this study uses the concept of egg candling in a dark place captured with a smartphone camera. The acquisition results are improved by image preprocessing using gray scaling, image enhancement (by Histogram Equalization), and segmentation of chicken egg image. The segmentation results were extracted using FOS with five parameters: mean, entropy, variance, skewness, and kurtosis. Based on the calculation of these parameters, it is graphed and shows the difference in patterns between fertile and infertile eggs. However, some eggs have a similar pattern, thus affecting the identification process. The identification process used neural networks by the backpropagation method for training and testing. The training results provide an accuracy value of 100% of all training data; however, 80% of the new test data obtained test results at testing. This test is carried out with 100 data, 50 each for training and test data. Based on the test results, which significantly affect the level of accuracy is the feature extraction method. FOS pattern in detecting the fertility of chicken eggs by BP Neural Network is still categorized as low, so it is necessary to improve methods to get maximum results.

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Segmentasi Citra Menggunakan Metode Watershed Transform Berdasarkan Image Enhancement Dalam Mendeteksi Embrio Telur

Cited by 12 publications

References 22 publications

Detection of Chicken Egg Embryos using BW Image Segmentation and Edge Detection Methods

Detection of Chicken Egg Embryos using BW Image Segmentation and Edge Detection Methods

Fish detection using morphological approach based-on k-means segmentation

Chicken Egg Fertility Identification using FOS and BP-Neural Networks on Image Processing

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