• Journal of Korea Multimedia Society
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• v.13 no.7
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• pp.1062-1075
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• 2010

In the medical image application the difference of intensity is widely used for the image segmentation and feature extraction, and a well known method is the threshold technique that determines a threshold value and generates a binary image based on the threshold. A frequently-used threshold technique is the Otsu algorithm that provides efficient processing and effective selection criterion for choosing the threshold value. However, we cannot get good segmentation results by applying the Otsu algorithm to chest X-ray images. It is because there are various organic structures around lung regions such as ribs and blood vessels, causing unclear distribution of intensity levels. To overcome the ambiguity, we propose in this paper an effective algorithm to extract pulmonary regions that utilizes the Otsu algorithm after removing the background of an X-ray image, constructs intensity-level maps, and uses them for segmenting the X-ray image. To verify the effectiveness of our method, we compared it with the existing 1-dimensional and 2-dimensional Otsu algorithms, and also the results by expert's naked eyes. The experimental result showed that our method achieved the more accurate extraction of pulmonary regions compared to the Otsu methods and showed the similar result as the naked eye's one.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.3
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• pp.224-230
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• 2002

In this paper, a counting algorithm hybridized with an adaptive automatic thresholding method based on Otsu's method and the algorithm that elongates markers obtained by the well-known watershed algorithm is proposed to enhance the exactness of the microcell counting in microscopic images. The proposed counting algorithm can be stated as follows. The transformed full image captured by CCD camera set up at microscope is divided into cropped images of m$\times$n blocks with an appropriate size. The thresholding value of the cropped image is obtained by Otsu's method and the image is transformed into binary image. The microbial cell images below prespecified pixels are regarded as noise and are removed in tile binary image. The smoothing procedure is done by the area opening and the morphological filter. Watershed algorithm and the elongating marker algorithm are applied. By repeating the above stated procedure for m$\times$n blocks, the m$\times$n segmented images are obtained. A superposed image with the size of 640$\times$480 pixels as same as original image is obtained from the m$\times$n segmented block images. By labeling the superposed image, the counting result on the image of microbial cells is achieved. To prove the effectiveness of the proposed mettled in counting the microbial cell on the image, we used Acinetobacter sp., a kind of ammonia-oxidizing bacteria, and compared the proposed method with the global Otsu's method the traditional watershed algorithm based on global thresholding value and human visual method. The result counted by the proposed method shows more approximated result to the human visual counting method than the result counted by any other method.

• Fisheries and Aquatic Sciences
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• v.4 no.1
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• pp.10-17
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• 2001

One serious problem among the troubles to identify objects in an optical microscopic image is contour background due to non-uniform light source and various transparency of samples. To solve this problem, this paper proposed an elimination method of the contour background and compensation technique as follows. First, Otsu's optimal thresholding method extracts pixels representing background. Second, bilinear interpolation finds non-deterministic background pixels among the sampled pixels. Third, the 2D cubic fitting method composes surface function from pivoted background pixels. Fourth, reconstruction procedure makes a contour image from the surface function. Finally, elimination procedure subtracts the approximated background from the original image. To prove the effectiveness of the proposed algorithm, this algorithm is applied to the yeast Zygosaccharomyces rouxii and ammonia-oxidizing bacteria Acinetobacter sp. Labeling by this proposed method can remove some noise and is more exact than labeling by only Otsu's method. Futhermore, we show that it is more effective for the reduction of noise.

• Fisheries and Aquatic Sciences
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• v.4 no.1
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• pp.1-9
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• 2001

This paper describes a procedure extracting feature vector of a target cell more precisely in the case of identifying specified cell. The classification of object type is based on feature vector such as area, complexity, centroid, rotation angle, effective diameter, perimeter, width and height of the object So, the feature vector plays very important role in classifying objects. Because the feature vectors is affected by noises and holes, it is necessary to remove noises contaminated in original image to get feature vector extraction exactly. In this paper, we propose the following method to do to get feature vector extraction exactly. First, by Otsu's optimal threshold selection method and morphological filters such as cleaning, filling and opening filters, we separate objects from background an get rid of isolated particles. After the labeling step by 4-adjacent neighborhood, the labeled image is filtered by the area filter. From this area-filtered image, feature vector such as area, complexity, centroid, rotation angle, effective diameter, the perimeter based on chain code and the width and height based on rotation matrix are extracted. To prove the effectiveness, the proposed method is applied for yeast Zygosaccharomyces rouxn. It is also shown that the experimental results from the proposed method is more efficient in measuring feature vectors than from only Otsu's optimal threshold detection method.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1371-1374
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• 1987

This paper presents a new automatic multithreshold selection method which is based on the threshold selection method proposed by Otsu. This method can overcome some of limitations of the Otsu's method. An optimal threshold is selected by the new criterion so as to maximize the separability in all subregions. To get multiple thresholds, the procedure may be recursively applied to the resultant classes which are determined by the proposed evaluation measure.

• International Journal of Automotive Technology
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• v.7 no.2
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• pp.227-235
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• 2006

In this paper a robust image processing method with modified Otsu algorithm to recognize the road lane for a real-time controlled autonomous vehicle is presented. The main objective of a proposed method is to drive an autonomous vehicle safely irrespective of road image qualities. For the steering of real-time controlled autonomous vehicle, a detection area is predefined by lane segment, with previously obtained frame data, and the edges are detected on the basis of a lane width. For stable as well as psudo-robust autonomous driving with "good", "shady" or even "bad" road profiles, the variable threshold with modified Otsu algorithm in the image histogram, is utilized to obtain a binary image from each frame. Also Hough transform is utilized to extract the lane segment. Whether the image is "good", "shady" or "bad", always robust and reliable edges are obtained from the algorithms applied in this paper in a real-time basis. For verifying the adaptability of the proposed algorithm, a miniature vehicle with a camera is constructed and tested with various road conditions. Also, various highway road images are analyzed with proposed algorithm to prove its usefulness.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.6
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• pp.587-595
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• 2011

In this paper, we focus on the issue of automatic selection for multi-level threshold, and we greatly improve the efficiency of Otsu's method for image segmentation based on genetic algorithm. We have investigated and evaluated the performance of the Otsu and Valley-emphasis threshold methods. Based on this observation we propose a method for automatic threshold method that segments an image into more than two regions with high performance and processing in real-time. Our paper introduced new peak detection, combines with evolution algorithm using MAGA (Modified Adaptive Genetic Algorithm) and HCA (Hill Climbing Algorithm), to find the best threshold automatically, accurately, and quickly. The experimental results show that the proposed evolutionary algorithm achieves a satisfactory segmentation effect and that the processing time can be greatly reduced when the number of thresholds increases.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.670-675
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• 2001

This paper is to propose image segmentation method based on chamfer algorithm. First, we get original image from CCD camera and transform it into gray image. Second, we extract maximum gray value of background and reconstruct and eliminate the background using surface fitting method and bilinear interpolation. Third, we subtract the reconstructed background from gray image to remove noises in gray image. Fourth, we transform the subtracted image into binary image using Otsu's optimal thresholding method. Fifth, we use morphological filters such as areaopen, opening, filling filter etc. to remove noises and isolated points. Sixth, we use chamfer distance or Euclidean distance to this filtered image. Finally, we use watershed algorithm and count microorganisms in image by labeling. To prove the effectiveness, we apply the proposed algorithm to one of Ammonia-oxidizing bacteria, Acinetobacter sp. It is shown that both Euclidean algorithm and chamfer algorithm show over-segmentation. But Chamfer algorithm shows less over-segmentation than Euclidean algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.466-471
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• 2007

Conventional thresholding methods including Otsu's thresholding method are based on the gray levels frequency histogram. But the gray levels frequency histogram is obtained by recomposing only frequency information from an input image, where frequency histogram dose not contain any other informations such as the distribution of gray levels and relation between gray levels. Therefore the methods using the gray levels frequency histogram occasionally present inappropriate threshold values because it cannot reflect informations of the given image sufficiently. In this paper, we define a correlation function of gray levels and propose a novel thresholding method using the gray levels frequency histogram and the spatial correlation information. The effectiveness of the proposed method will be shown through comparison with Otsu's thresholding method.

• Ecology and Resilient Infrastructure
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• v.10 no.4
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• pp.135-142
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• 2023

A various technology of remote sensing and image analysis are applied to study landscape changes and their influencing factors in stream corridors. We developed a method to detect landscape changes over time by calculating the optical index using multispectral images taken from satellites at various time points, calculating the threshold to delineate the boundaries of water bodies, and creating binarized maps into land and water areas. This method was applied to the upstream reach of the weirs in the Geumgang River to track changes in the sandbar formed by the opening of the weir gate. First, we collected multispectral images with a resolution of 10 m × 10 m taken from the Sentinel-2 satellite at various times before and after the opening of the dam in the Geumgang River. The normalized difference water index (NDWI) was calculated using the green light and near-infrared bands from the collected images. The Otsu's threshold of NDWI calculated to delineate the boundary of the water body ranged from -0.0573 to 0.1367. The boundary of the water area determined by remote sensing matched the boundary in the actual image. A map binarized into water and land areas was created using NDWI and the Otsu's threshold. According to these results of the developed method, it was estimated that a total of 379.7 ha of new sandbar was formed by opening the three weir floodgates from 2017 to 2021 in the longitudinal range from Baekje Weir to Daecheong Dam on the Geumgang River. The landscape detection method developed in this study is evaluated as a useful method that can obtain objective results with few resources over a wide spatial and temporal range.