• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.670-675
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• 2015

Medical imaging is one of the most powerful tools for gaining information about internal organs and tissues. It is a challenging task to develop sophisticated image analysis methods in order to improve the accuracy of diagnosis. The objective of this paper is to develop a Computer Aided Diagnostics (CAD) scheme for Brain Tumour detection from Magnetic Resonance Image (MRI) using active contour models and to investigate with several approaches for improving CAD performances. The problem in clinical medicine is the automatic detection of brain Tumours with maximum accuracy and in less time. This work involves the following steps: i) Segmentation performed by Fuzzy Clustering with Level Set Method (FCMLSM) and performance is compared with snake models based on Balloon force and Gradient Vector Force (GVF), Distance Regularized Level Set Method (DRLSE). ii) Feature extraction done by Shape and Texture based features. iii) Brain Tumour detection performed by various tree classifiers. Based on investigation FCMLSM is well suited segmentation method and Random Forest is the most optimum classifier for this problem. This method gives accuracy of 97% and with minimum classification error. The time taken to detect Tumour is approximately 2 mins for an examination (30 slices).

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.482-486
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• 2009

Scale-invariant feature is an effective method for retrieving and classifying images. In this study, we analyze a scale-invariant planar curve features for developing 2D shapes. Scale-space filtering is used to determine contour structures on different scales. However, it is difficult to track significant points on different scales. In mathematics, curvature is considered to be fundamental feature of a planar curve. However, the curvature of a digitized planar curve depends on a scale. Therefore, automatic scale detection for curvature analysis is required for practical use. We propose a technique for achieving automatic scale detection based on difference of curvature. Once the curvature values are normalized with regard to the scale, we can calculate difference in the curvature values for different scales. Further, an appropriate scale and its position are detected simultaneously, thereby avoiding tracking problem. Appropriate scales and their positions can be detected with high accuracy. An advantage of the proposed method is that the detected significant points do not need to be located in the same contour. The validity of the proposed method is confirmed by experimental results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.2
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• pp.112-123
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• 1990

This paper presents a vertex-detecting of Hanguel patterns using nested contour shape. Inputed binary character patterns are transformed by distance transformation method and make a new file of transferred data by analysis of charactersitcs. A new vertex-detecting algorithm for recognizing Hanguel patterns using the two data files is proposed. This algorithm is able to reduce the projecting parts of Hanguel pattern, separate the connecting parts between different strokes, set the code number by transformed value of coorked features. It makes the output of results in order to apply the Hanguel recognition.

• The Korean Journal of Cytopathology
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• v.2 no.1
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• pp.1-7
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• 1991

Cytologic findings from five cases with variable types of Hodgkln's disease were reviewed with special emphasis on the Reed-Sternberg (R-S) cells and their variants. Typical R-S and Hodgkin's cells were mono- or binucleated, and nuclei had rounded smooth contour, Acidophilic prominent nucleoli with perinucleolar halo were conspicuous in comparison to typical Reed-Sternberg cells, L & H (lymphocytic and histiocytic) cells in the lymphocyte predominant type tended to show pop-corn like irregular nuclear contour and to lack the prominent nucleoli. Lacunar cells in the nodular sclerosis type had multilobated nuclei with prominent acidophilic nucleoli. There was no prominent perinucleolar halo in L & H and lacunar cells. In conjuction with the number of Reed-Sternberg cells and back ground findings observed on the smears, the characteristic features of R-S cells and their variants allowed to make typing of Hodgkin's disease.

• The Transactions of the Korea Information Processing Society
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• v.3 no.3
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• pp.683-695
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• 1996

In this paper, we implemented the content-based image retrieval system that indexes and retrieves the images by acquiring contour information of images and by extracting lines from the object. For this purpose, we proposed the advanced line extraction algorithm called FSLHT(Flexible SLHT) which covers drawback of SLHT(Straight Line Hough Transform)andapplied aDP(Dynamic Programming)algorithm to getadesirable similarity ofimages by lines. We estimated the contour features as a key value of sampled region to compensate for the problem that image contours are heavily depend on the noise. When performing the Hough transform we calculated the directionality based on the perceptual organization and transformed according to this direction to overcome the problem of time consuming and discontinuity.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.737-747
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• 2019

Shape description is an important and fundamental issue in content-based image retrieval (CBIR), and a number of shape description methods have been reported in the literature. For shape description, both global information and local contour variations play important roles. In this paper a new included-angular ternary pattern (IATP) based shape descriptor is proposed for shape image retrieval. For each point on the shape contour, IATP is derived from its neighbor points, and IATP has good properties for shape description. IATP is intrinsically invariant to rotation, translation and scaling. To enhance the description capability, multiscale IATP histogram is presented to describe both local and global information of shape. Then multiscale IATP histogram is combined with included-angular histogram for efficient shape retrieval. In the matching stage, cosine distance is used to measure shape features' similarity. Image retrieval experiments are conducted on the standard MPEG-7 shape database and Swedish leaf database. And the shape image retrieval performance of the proposed method is compared with other shape descriptors using the standard evaluation method. The experimental results of shape retrieval indicate that the proposed method reaches higher precision at the same recall value compared with other description method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.171-176
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• 2022

CNN is a type of deep learning and is a neural network used to process images or image data. The filter traverses the image and extracts features of the image to distinguish the image. Deep learning has the characteristic that the more data, the better models can be made, and CNN uses a method of artificially increasing the amount of data by means of data augmentation such as rotation, zoom, shift, and flip to compensate for the weakness of less data. When learning CNN, we would like to check whether outline image learning is helpful in improving performance compared to conventional data augmentation techniques.

• The Transactions of the Korea Information Processing Society
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• v.4 no.1
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• pp.54-66
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• 1997

Efficient retrieval of image data is an important research issue in multimedia database. This paper proposes a new approach to a content-based image retrieval which allows queries to be composed of the local texture patterns and the slope components of contour points. The texture patterns extracted from the source image using the graylevel co-occurrence matrix and the slope components of contour points extracted from the binary image are converted into a internal feature representation of reduced dimensionality which preserves the perceptual similarity and those features can be used in creating efficient indexing structures for a content-based image retrieval. Experimental results of the image retrievalare presented to illustrate the usefulness of this approach that demonstrates the precision 82%, the recall 87% and the average rang 3.3 in content-based image data retrieval.

• The KIPS Transactions:PartB
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• v.12B no.5 s.101
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• pp.527-534
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• 2005

Rotation and scale variations make it difficult to solve the problem of shape description and recognition because these variations change the location of points composing the shape. However, some geometric Invariant points and the relations among them are not changed by these variations. Therefore, if points in image space depicted with the r-y coordinates system can be transformed into a new coordinates system that are invariant to rotation and scale, the problem of shape description and recognition becomes easier. This paper presents a shape description method via transformation from the image space into the invariant feature space having two axes: representing relative distance from a centroid and contour segment curvature(CSC). The relative distance describes how far a point departs from the centroid, and the CSC represents the degree of fluctuation in a contour segment. After transformation, mesh features were used to describe the shape mapped onto the feature space. Experimental results show that the proposed method is robust to rotation and scale variations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.2363-2374
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• 2016

Communication occurs through verbal elements, which usually involve language, as well as non-verbal elements such as facial expressions, eye contact, and gestures. In particular, among these non-verbal elements, gestures are symbolic representations of physical, vocal, and emotional behaviors. This means that gestures can be signals toward a target or expressions of internal psychological processes, rather than simply movements of the body or hands. Moreover, gestures with such properties have been the focus of much research for a new interface in the NUI/NUX field. In this paper, we propose a method for recognizing the number of fingers and detecting the hand region based on the depth information and geometric features of the hand for application to an NUI/NUX. The hand region is detected by using depth information provided by the Kinect system, and the number of fingers is identified by comparing the distance between the contour and the center of the hand region. The contour is detected using the Suzuki85 algorithm, and the number of fingers is calculated by detecting the finger tips in a location at the maximum distance to compare the distances between three consecutive dots in the contour and the center point of the hand. The average recognition rate for the number of fingers is 98.6%, and the execution time is 0.065 ms for the algorithm used in the proposed method. Although this method is fast and its complexity is low, it shows a higher recognition rate and faster recognition speed than other methods. As an application example of the proposed method, this paper explains a Secret Door that recognizes a password by recognizing the number of fingers held up by a user.