K-Means Clustering for Egg Embryo's Detection Based-on Statistical Feature Extraction Approach of Candling Eggs Image

Saifullah, Shoffan

doi:10.22441/sinergi.2021.1.006

Cited by 11 publications

(7 citation statements)

References 31 publications

(47 reference statements)

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“…This resizing aims to change the size of the image into pixels. In the research, the size is changed to be smaller than the original image size (Saifullah, 2020a), aiming for image processing at the time of segmentation being faster and not changing the information contained in it. Meanwhile, reshape is used to modify the dimensions of the original matrix to be as desired.…”

Section: Image Preprocessingmentioning

confidence: 99%

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

2021

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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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Section: Image Preprocessingmentioning

confidence: 99%

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

2021

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“…This feature is able to classify omega 3 and leghorn eggs with an accuracy of 91% [22]. In previous studies [4], [12], [17], it was stated that existing features needed to be reduced. Thus, this study reduced GLCM to FOS (using five parameters).…”

Section: Introductionmentioning

confidence: 98%

“…In feature extraction, this detection uses GLCM with various features, including five or seven features. The results of feature extraction are inputted into fertility classification (embryo presence) using the K-means method [7], [9], [17], Backpropagation, SVM [18], and Naïve Bayes. In this study, the segmentation used is when cropping the image according to the egg object in the image preprocessing.…”

Section: Introductionmentioning

confidence: 99%

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

Saifullah

Suryotomo

2021

RESTI

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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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“…This detection process uses image processing to determine the presence of embryos in eggs [6]. The identification process [7] in previous studies used machine learning methods [8], both supervised [9] and unsupervised learning [10]. The process starts by taking the image used as a dataset.…”

Section: Introductionmentioning

confidence: 99%

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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K-Means Clustering for Egg Embryo's Detection Based-on Statistical Feature Extraction Approach of Candling Eggs Image

Cited by 11 publications

References 31 publications

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

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

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

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

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