• Journal of Environmental Science International
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• v.25 no.9
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• pp.1289-1297
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• 2016

The walls of guard cells have many specialized features. Guard cells are present in the leaves of bryophytes, ferns, and almost all vascular plants. However, they exhibit considerable morphological diversities. There are two types of guard cells: the first type is found in a few monocots, such as palms and corn, and the other is found in most dicots, many monocots, mosses, ferns, and gymnosperms. In corns, guard cells have a characteristic dumbbell shape with bulbous ends. Most dicot and monocot species have kidney-shaped guard cells that have an elliptical contour with a pore at its center. Although subsidiary cells are common in species with kidney-shaped stomata, they are almost always absent in most of the other plants. In this study, there were many different stomatal features that were associated with kidney-shaped guard cells, but not dumbbell shaped guard cells, which are present in most grasses, such as cereals. Each plant investigated exhibited different characteristic features and most of these plants had kidney-shaped guard cells. However, the guard cells of Chamaesyce supina Mold, were often more rectangular than kidney-shaped. In contrast, Sedum sarmentosum guard cells were of the sink ensiform type and in Trifolium repens, the guard cells exhibited a more rhombic shape. Therefore, kidney-shaped guard cells could be divided into a number of subtypes that need to be investigated further.

• The Korean Journal of Cytopathology
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• v.8 no.1
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• pp.1-10
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• 1997

The fine needle aspiration biopsy(FNAB) has become a popular method to diagnose mass lesions of the liver. Although many reports have listed FNAB criteria to be used to diagnose hepatocellular carcinoma(HCC), a diagnostic dilemma still exists at the extreme ends of the spectrum, particularly for well differentiated HCC. The authors reviewed a series of FNAB specimens of the liver to distinguish well differentiated HCC from nonneoplastic liver. Fifteen cytologic features were examined in this study: high cellularity, large sheet formation, trabecular pattern, acinar pattern, dispersed pattern, irregular arrangement, increased nuclear/cytoplasmic ratio, naked nuclei, irregular chromatin, irregular nuclear contour, multinucleation, uniform macronucleoli, multiple nuclei, uniform small cytoplasm and monotony of atypia. These features were examined in a series of 76 FNAB specimens. Fifty two specimens were from patients with HCC and 24 specimens were from patients with nonneoplastic lesion or tumors other than HCC containg adequate amount of nonneoplastic hepatocytes in smear. All specimens were coded as to the presence or absence of the above cytologic features. With the use of step-wise logistic regression analysis, three features were identified as the key cytologic features predictive of HCC: irregular chromatin, monotony of atypia and absence of large sheet formation. When these criteria were used, the sensitivity diagnosing HCC by FNAB was 94.2%, specificity 100%, positive predictive value 100% and negative predictive value was 88.9%.

• Korean Journal of Cognitive Science
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• v.27 no.3
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• pp.355-376
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• 2016

We used holes defined by color similarity (Experiment 1) and binocular disparity (Experiment 2) to study how the inner contour of an object (i.e., boundary of a hole in it) is encoded in visual working memory. Many studies in VWM have shown that an object's boundary properties can be integrated with its surface properties via their shared spatial location, yielding an object-based encoding benefit. However, encoding of the hole contours has rarely been tested. We presented objects (squares or circles) containing a bar under a change detection paradigm, and relevant features to be remembered were the color of objects and the orientation of bars (or holes). If the contour of a hole belongs to the surrounding object rather than to the hole itself, the object-based feature binding hypothesis predicts that the shape of it can be integrated with color of an outer object, via their shared spatial location. Thus, in the hole display, change detection performance was expected to better than in the conjunction display where orientation and color features to be remembered were assigned to different parts of a conjunction object, and comparable to that in a single bar display where both orientation and color were assigned into a single bar. However, the results revealed that performance in the hole display did not differ from that in the conjunction display. This suggests that the shape of holes is not automatically encoded together with the surface properties of the outer object via object-based feature binding, but encoded independently from the surrounding object.

• Journal of Gastric Cancer
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• v.22 no.2
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• pp.120-134
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• 2022

Purpose: This study aimed to identify prognostic factors for patients with distant lymph node-involved gastric cancer (GC) using a machine learning algorithm, a method that offers considerable advantages and new prospects for high-dimensional biomedical data exploration. Materials and Methods: This study employed 79 features of clinical pathology, laboratory tests, and therapeutic details from 289 GC patients whose distant lymphadenopathy was presented as the first episode of recurrence or metastasis. Outcomes were measured as any-cause death events and survival months after distant lymph node metastasis. A prediction model was built based on possible outcome predictors using a random survival forest algorithm and confirmed by 5×5 nested cross-validation. The effects of single variables were interpreted using partial dependence plots. A contour plot was used to visually represent survival prediction based on 2 predictive features. Results: The median survival time of patients with GC with distant nodal metastasis was 9.2 months. The optimal model incorporated the prealbumin level and the prothrombin time (PT), and yielded a prediction error of 0.353. The inclusion of other variables resulted in poorer model performance. Patients with higher serum prealbumin levels or shorter PTs had a significantly better prognosis. The predicted one-year survival rate was stratified and illustrated as a contour plot based on the combined effect the prealbumin level and the PT. Conclusions: Machine learning is useful for identifying the important determinants of cancer survival using high-dimensional datasets. The prealbumin level and the PT on distant lymph node metastasis are the 2 most crucial factors in predicting the subsequent survival time of advanced GC.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.5
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• pp.39-46
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• 2007

Research on human identification in ubiquitous environment has recently attracted a lot of attention. As one of those research, gait recognition is an efficient method of human identification using physical features of a walking person at a distance. In this paper, we present a human shape extraction and tracking for gait recognition using geodesic active contour models(GACMs) combined with mean shift algorithm The active contour models (ACMs) are very effective to deal with the non-rigid object because of its elastic property. However, they have the limitation that their performance is mainly dependent on the initial curve. To overcome this problem, we combine the mean shift algorithm with the traditional GACMs. The main idea is very simple. Before evolving using level set method, the initial curve in each frame is re-localized near the human region and is resized enough to include the targe region. This mechanism allows for reducing the number of iterations and for handling the large object motion. The proposed system is composed of human region detection and human shape tracking modules. In the human region detection module, the silhouette of a walking person is extracted by background subtraction and morphologic operation. Then human shape are correctly obtained by the GACMs with mean shift algorithm. In experimental results, the proposed method show that it is extracted and tracked efficiently accurate shape for gait recognition.

• Journal of Korea Multimedia Society
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• v.15 no.9
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• pp.1075-1085
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• 2012

In clinical research using medical images, the image segmentation is one of the most important processes. Especially, the hippocampal atrophy is helpful for the clinical Alzheimer diagnosis as a specific marker of the progress of Alzheimer. In order to measure hippocampus volume exactly, segmentation of the hippocampus is essential. However, the hippocampus has some features like relatively low contrast, low signal-to-noise ratio, discreted boundary in MRI images, and these features make it difficult to segment hippocampus. To solve this problem, firstly, We selected region of interest from an experiment image, subtracted a original image from the negative image of the original image, enhanced contrast, and applied anisotropic diffusion filtering and gaussian filtering as preprocessing. Finally, We performed an image segmentation using two level set methods. Through a variety of approaches for the validation of proposed hippocampus segmentation method, We confirmed that our proposed method improved the rate and accuracy of the segmentation. Consequently, the proposed method is suitable for segmentation of the area which has similar features with the hippocampus. We believe that our method has great potential if successfully combined with other research findings.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.4
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• pp.370-380
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• 2002

This paper proposes an ATR (Automatic Target Recognition) algorithm for identifying non-occluded and occluded military vehicles in natural FLIR (Forward Looking InfraRed) images. After segmenting a target, a radial function is defined from the target boundary to extract global shape features. Also, to extract local shape features of upper region of a target, a distance function is defined from boundary points and a line between two extreme points. From two functions and target contour, four global and four local shape features are proposed. They are much more invariant to translation, rotation and scale transform than traditional feature sets. In the experiments, we show that the proposed feature set is superior to the traditional feature sets with respect to the similarity-transform invariance and recognition performance.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.2
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• pp.855-860
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• 2014

Background: The purpose of this study was to assess the relationship between different sonographic features of papillary thyroid carcinoma (PTC) on high-frequency ultrasound and cervical lymph node metastasis (CLNM). Materials and Methods: We enrolled 548 patients who underwent initial surgery for PTC between May 2011 and December 2012 in our hospital at diagnosis. The sonographic features of 513 PTC nodules in 513 eligible patients, who had single PTC nodules in their thyroid glands, were retrospectively investigated. All patients with a suspect malignant nodule (d<0.5cm) among multiple nodules were initially diagnosed by fine-needle aspiration biopsy (FNAB) to ascertain if the suspect nodule was PTC. The final diagnosis of all the thyroid nodules and existence of CLNM were based on postoperative pathology. Patients were divided into two groups: a positive group with CLNM (224 nodules) and a negative group without CLNM (289 nodules). The following factors were investigated: gender, age, echogenicity, echotexture, size, shape, location, margin, contour, calcification morphology, distance between the nodule and pre- or post-border of the thyroid capsule, vascularity and the differences between the two groups. Results: Correlation analysis showed that shorter distances between the nodule and pre- or postborder of thyroid capsule resulted in greater risk of CLNM (Spearman correlation coefficient=-0.22, p<0.0001). The significant factors in multivariate analysis were age<45yrs, larger size (d>1cm), "wider than tall" shape, extrathyroid extension and mixed flow (internal and peripheral) (p<0.05, OR=0.406, 2.093, 0.461, 1.610, 1.322). Conclusions: Significant sonographic features of PTC nodules in preoperative high-frequency ultrasound are crucial for predicting CLNM.

• Journal of KIISE:Databases
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• v.32 no.5
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• pp.498-508
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• 2005

A shape data is used one oi most important feature for image retrieval as data to reflect meaning of image. Especially, structural feature of shape is widely studied because it represents primitive properties of shape and relation information between basic units well. However, most structural features of shape have the problem that it is not able to guarantee an efficient search time because the features are expressed as graph or tree. In order to solve this problem, we generate curvature-based morphological graph, End design key to cluster shapes from this graph. Proposed this graph have contour features and morphological features of a shape. Shape retrieval is accomplished by stages. We reduce a search space through clustering, and determine total similarity value through pattern matching of external curvature. Various experiments show that our approach reduces computational complexity and retrieval cost.

• Journal of KIISE:Software and Applications
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• v.34 no.1
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• pp.18-29
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• 2007

In this paper we present a system that classifies brain MR images by using 2 level decision tree learning. There are two kinds of information that can be obtained from images. One is the low-level features such as size, color, texture, and contour that can be acquired directly from the raw images, and the other is the high-level features such as existence of certain object, spatial relations between different parts that must be obtained through the interpretation of segmented images. Learning and classification should be performed based on the high-level features to classify images according to their semantic meaning. The proposed system applies decision tree learning to each level separately, and the high-level features are synthesized from the results of low-level classification. The experimental results with a set of brain MR images with tumor are discussed. Several experimental results that show the effectiveness of the proposed system are also presented.