• Journal of Bio-Environment Control
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• v.1 no.1
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• pp.37-51
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• 1992

Quantizing and extracting visual features of mushroom(Lentinus edodes L.) are crucial to the sorting and grading automation, the growth state measurement, and the dried performance indexing. A computer image processing system was utilized for the extraction and measurement of visual features of front and back sides of the mushroom. The image processing system is composed of the IBM PC compatible 386DK, ITEX PCVISION Plus frame grabber, B/W CCD camera, VGA color graphic monitor, and image output RGB monitor. In this paper, an automatic thresholding algorithm was developed to yield the segmented binary image representing skin states of the front and back sides. An eight directional Freeman's chain coding was modified to solve the edge disconnectivity by gradually expanding the mask size of 3$\times$3 to 9$\times$9. A real scaled geometric quantity of the object was directly extracted from the 8-directional chain element. The external shape of the mushroom was analyzed and converted to the quantitative feature patterns. Efficient algorithms for the extraction of the selected feature patterns and the recognition of the front and back side were developed. The developed algorithms were coded in a menu driven way using MS_C language Ver.6.0, PC VISION PLUS library fuctions, and VGA graphic functions.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.50-53
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• 2016

Image processing techniques play an increasingly important role in many aspects of our daily life. For example, it has been shown to improve agricultural productivity in a number of ways such as plant pest detecting or fruit grading. However, massive quantities of images generated in real-time through multi-devices such as remote sensors during monitoring plant growth lead to the challenges of big data. Meanwhile, most current image processing systems are designed for small-scale and local computation, and they do not scale well to handle big data problems with their large requirements for computational resources and storage. In this paper, we have proposed an IPABigData (Image Processing Algorithm BigData) platform which provides algorithms to support large-scale image processing in agriculture based on Hadoop framework. Hadoop provides a parallel computation model MapReduce and Hadoop distributed file system (HDFS) module. It can also handle parallel pipelines, which are frequently used in image processing. In our experiment, we show that our platform outperforms traditional system in a scenario of image segmentation.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1486-1495
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• 2020

The analysis of digital microscopy images plays a vital role in computer-aided diagnosis (CAD) and prognosis. The main purpose of this paper is to develop a machine learning technique to predict the histological grades in prostate biopsy. To perform a multiclass classification, an AI-based deep learning algorithm, a multichannel convolutional neural network (MCCNN) was developed by connecting layers with artificial neurons inspired by the human brain system. The histological grades that were used for the analysis are benign, grade 3, grade 4, and grade 5. The proposed approach aims to classify multiple patterns of images extracted from the whole slide image (WSI) of a prostate biopsy based on the Gleason grading system. The Multichannel Convolution Neural Network (MCCNN) model takes three input channels (Red, Green, and Blue) to extract the computational features from each channel and concatenate them for multiclass classification. Stain normalization was carried out for each histological grade to standardize the intensity and contrast level in the image. The proposed model has been trained, validated, and tested with the histopathological images and has achieved an average accuracy of 96.4%, 94.6%, and 95.1%, respectively.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.10
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• pp.377-382
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• 2018

This study deals with internal state and tissue density analysis methods for red ginseng grade determination. For internal measurement of red ginseng, there have been various studies on nondestructive testing methods since the 1990s, It was difficult to grasp the most important inner hole and inside whites in the grading. So in this study, we developed a closed capturing device for infra-red illumination environment, and developed an internal measurement system that can detect the presence and diameter of inner hole and inside whites. Made devices consisted of infrared lights with a high transmission rate of red ginseng in 920 nanometer wave band, a infra-red camera and a Y axis actuator with a red ginseng automatically controlled focus on the camera. The proposed algorithm performs an auto-focus system on the Y-axis actuator to automatically adjust the sharp focus of the object according to the size and thickness. Then red ginseng is rotated $360^{\circ}$ at $1^{\circ}$ intervals and 360 total images are acquired, and reconstructed as a sinogram through Radon transform and Back-projection algorithm was performed to acquire internal images of red ginseng. As a result of the algorithm, it was possible to acquire internal cross-sectional image regardless of the thickness and shape of red ginseng. In the future, if more than 10,000 different shapes and sizes of red ginseng internal cross-sectional image are acquired and the classification criterion is applied, it can be used as a reliable automated ginseng grade automatic measurement method.

• Journal of Biosystems Engineering
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• v.30 no.6 s.113
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• pp.340-346
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• 2005

Major quality features of the beef carcass in most countries including Korea are size, marbling state of the lean tissue, color of the fat and lean tissue, and thickness of back fat of the 13th rib. To evaluate the beef quality, extracting loin parts from the sectional image of the 13th beef rib is crucial and is the first step. However, because of the inhomogeneous distribution and fuzzy pattern of the fat and lean tissues on the beef cut, it is difficult to extract automatically the proper contour of the lean tissue. In this paper, a prototype mobile beef quality measurement system, which can be implemented practically at the beef processing site was developed. The developed system was composed of the hand held image acquisition unit and mobile processing unit mounted with touch-pad screen. Algorithms to extract the boundary of the lean tissue and a proper tool to evaluate the marbling status have been developed using color image processing. The boundary extraction algorithm showed successful results for the beef cuts with simple and moderate patterns of the lean tissue and fat. However, it had some difficulty in eliminating complex pattern of the extraneous tissues adhered to the lean tissue in the boundary extraction. The developed algorithms were implemented to the prototype mobile processing unit.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.4
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• pp.77-89
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• 2024

Knee osteoarthritis (OA) is a very common musculoskeletal disorder worldwide. The assessment of knee osteoarthritis, which requires a rapid and accurate initial diagnosis, is determined to be different depending on the currently dispersed classification system, and each classification system has different criteria. Also, because the medical staff directly sees and reads the X-ray pictures, it depends on the subjective opinion of the medical staff, and it takes time to establish an accurate diagnosis and a clear treatment plan. Therefore, in this study, we designed the stenosis length measurement algorithm and Osteophyte detection and length measurement algorithm, which are the criteria for determining the knee osteoarthritis grade, separately using CNN, which is a deep learning technique. In addition, we would like to create a grading system that integrates and complements the existing classification system and show results that match the judgments of actual medical staff. Based on publicly available OAI (Osteoarthritis Initiative) data, a total of 9,786 knee osteoarthritis data were used in this study, eventually achieving an Accuracy of 69.8% and an F1 score of 76.65%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.446-452
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• 2021

As a higher standard for food consumption is required, the consumption of chicken meat that can satisfy the subdivided food preferences is increasing. In March 2003, the quality criteria for chicken carcasses notified by the Livestock Quality Assessment Service suggested quality grades according to fecal contamination and the size and weight of blood and bruises. On the other hand, it is too difficult for human inspection to qualify mass products, which is key to maintaining consistency for grading thousands of chicken carcasses. This paper proposed the computer vision algorithm as a non-destructive inspection, which can identify chicken carcass parts according to the detailed standards. To inspect the chicken carcasses conveyed at high speed, the image calibration was involved in providing robustness to the side effect of external lighting interference. The separation between chicken and background was achieved by a series of image processing, such as binarization based on Expectation Maximization, Erosion, and Labeling. In terms of shape analysis of chicken carcasses, the features are presented to reveal geometric information. After applying the algorithm to 78 chicken carcass samples, the algorithm was effective in segmenting chicken carcass against a background and analyzing its geometric features.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.318-326
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• 2005

This paper presents the image feature extraction method and the image classification technique to select the harmful image flowed from the Internet by grade of image contents such as harmlessness, sex-appealing, harmfulness (nude), serious harmfulness (adult) by the characteristic of the image. In this paper, we suggest skin area detection technique to recognize whether an input image is harmful or not. We also propose the ROI detection algorithm that establishes region of interest to reduce some noise and extracts harmful degree effectively and defines the characteristics in the ROI area inside. And this paper suggests the multiple-SVM training method that creates the image classification model to select as 4 types of class defined above. This paper presents the multiple-SVM classification algorithm that categorizes harmful grade of input data with suggested classification model. We suggest the skin likelihood image made of the shape information of the skin area image and the color information of the skin ratio image specially. And we propose the image feature vector to use in the characteristic category at a course of traininB resizing the skin likelihood image. Finally, this paper presents the performance evaluation of experiment result, and proves the suitability of grading image using image feature classification algorithm.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.2
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• pp.867-870
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• 2014

Purpose: This study used receiver operating characteristic curve to analyze Surveillance, Epidemiology and End Results (SEER) ependymoma data to identify predictive models and potential disparity in outcome. Materials and Methods: This study analyzed socio-economic, staging and treatment factors available in the SEER database for ependymoma. For the risk modeling, each factor was fitted by a Generalized Linear Model to predict the outcome ('brain and other nervous systems' specific death in yes/no). The area under the receiver operating characteristic curve (ROC) was computed. Similar strata were combined to construct the most parsimonious models. A random sampling algorithm was used to estimate the modeling errors. Risk of ependymoma death was computed for the predictors for comparison. Results: A total of 3,500 patients diagnosed from 1973 to 2009 were included in this study. The mean follow up time (S.D.) was 79.8 (82.3) months. Some 46% of the patients were female. The mean (S.D.) age was 34.4 (22.8) years. Age was the most predictive factor of outcome. Unknown grade demonstrated a 15% risk of cause specific death compared to 9% for grades I and II, and 36% for grades III and IV. A 5-tiered grade model (with a ROC area 0.48) was optimized to a 3-tiered model (with ROC area of 0.53). This ROC area tied for the second with that for surgery. African-American patients had 21.5% risk of death compared with 16.6% for the others. Some 72.7% of patient who did not get RT had cerebellar or spinal ependymoma. Patients undergoing surgery had 16.3% risk of death, as compared to 23.7% among those who did not have surgery. Conclusion: Grading ependymoma may dramatically improve modeling of data. RT is under used for cerebellum and spinal cord ependymoma and it may be a potential way to improve outcome.

• Journal of Biosystems Engineering
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• v.27 no.4
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• pp.341-348
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• 2002

Quality evaluation of red ginsengs is determined by outer shape and inner qualities. Especially, the inner qualities are main grading criteria. Currently, red ginsengs are classified into 3-grades; heaven, earth and good. The best heaven grade must not include inner holes and sponge tissues. This study was conducted to develop a red ginseng sorting system using x-ray image processing technique. Because of lens characteristic, gray values of the central region in the x-ray image are higher and gradually decreased towards the edge regions. This difference of gray values gives trouble in segmentation and detection of inner holes in red ginseng image, so preprocessing technique is necessary. The preprocessing was done by subtracting source image from an empty background image. But, simple subtraction was not quite appropriate because of too small contrast between inner holes and sound part. Scaled subtraction images were obtained by multiplying all gray values by some numbers. However this method could not help to set threshold value because the gray values of root part are generally lower than body part when red ginseng is exposed to the x-ray. To determine threshold value for detecting inner holes, an algorithm was developed by increasing overall gray values of less clear images.